A novel fuzzy logic approach to online exposure time calculation of line scan cameras in industrial inspection

Transcription

1 Int. J. Modelling, Identification and Control, Vol. 21, No. 1, A novel fuzzy logic approach to online exposure time calculation of line scan cameras in industrial inspection Hossein Farid Ghassem Nia 1*, Huosheng Hu 2, John Q. Gan 3 School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, United Kingdom; 1 hfarid@essex.ac.uk 2 hhu@essex.ac.uk 3 jqgan@essex.ac.uk * The author for correspondence Abstract: Exposure time calculation is an essential issue for digital cameras used in sensitive and secure industrial inspections to capture high quality image and consequently realise reliable performance of image processing algorithms. In this paper, we propose a novel exposure time calculation algorithm for line scan cameras in sensitive industrial inspection to deal with the problems caused by a harsh and dynamic environment. Since failure in capturing high quality images will cause the image processing algorithm to fail, the proposed algorithm will automatically examine whether there is a need to the exposure time and estimate the optimal exposure time. The median of scanned line and the centre of histogram distribution are input to a fuzzy system to determine the need for exposure time modification and optimize exposure calculation. Experiments were conducted to compare the proposed method with other existing methods, and the results have demonstrated the effectiveness and robustness of the proposed method. Keywords: Industrial inspection, Line scan camera, Fuzzy logic, Exposure time calculation. Reference to this paper should be made as follows: Hossein Farid Ghassem Nia, Huosheng Hu, John Q. Gan A novel fuzzy logic approach to online exposure time calculation of line scan cameras in industrial inspection, International Journal of Modelling, Identification and Control, Vol. x, No. x, pp. xxx-xxx. Biography notes: Hossein Farid Ghassem Nia is PhD candidate and KTP associate in School of Computer Science and Electronic Engineering at University of Essex, United Kingdom. He received his Bachelor degree in Computer Science from University of Qazvin in 2008 and Master degree from University of Essex in His research interests include Robotics, Computer Vision and Industrial Automation. Huosheng Hu is a Professor in School of Computer Science and Electronic Engineering at the University of Essex, United Kingdom, leading the human-centred robotics research. His research interests include behaviour-based robotics, humanrobot interaction, embedded systems, mechatronics, pervasive computing, and service robots. He has published over 380 papers in journals, books and conferences in these areas, and received a number of best paper awards. He is a Fellow of IET, a Fellow of InstMC, a senior member of IEEE and ACM. He currently serves as an Editor-in-Chief for International Journal of Automation and Computing, Founding Editor-in-Chief of Robotics Journal and an Executive Editor for International Journal of Mechatronics and Automation. John Q. Gan is a Professor in School of Computer Science and Electronic Engineering at University of Essex, United Kingdom. His research interests include neurofuzzy computation, brain computer interfaces, robotics and intelligent systems, pattern recognition, and signal processing. He has co-authored a book and published Copyright 2013 Inderscience Enterprises Ltd /IJMIC

2 A novel fuzzy logic approach to online exposure time calculation of line scan cameras in industrial inspection 9 over 150 research papers. He is Associate Editor for IEEE Transactions on Cybernetics. 1. Introduction Digital cameras are currently essential part of industrial inspection and automation. There are two major types of digital cameras, 2D area cameras and line scan cameras. Both are widely used in image processing and industrial inspection applications. Most digital cameras in the market are equipped with Automatic Exposure Time (AET) algorithms that can adjust the exposure time on the fly. However, due to constraints of AET, a fixed exposure time is preferred in many industrial inspection applications to satisfy the real-time requirements. AET calculation is an important part of digital cameras, and aims to provide a platform where cameras can obtain images with good contrast in various lighting conditions and changing environments. On the other hand, AET covers the maximum range of the image to achieve a sense of balance in contrast and histogram of the captured image. Apart from exposure time, there are few parameters that can be controlled to optimize image quality, such as aperture and gains. The in the aperture normally leads to in depth of focus, which therefore is not practical for realtime applications. In this paper, we assume that aperture and gains are constant. A number of methods have been proposed for AET calculation, majority of which are for area cameras. The main approach to exposure calculation in line scan cameras is manual adjustment through trial and error. Manual adjustment of exposure can be effective as long as the environment, target and illumination do not during operation. However, a minor in any of these conditions will make a manually adjusted line scan camera function improperly. Another approach is to make a line scan camera behave like an area camera with unknown image length. In this approach, a fixed number of lines (fixed size window) will be saved into a buffer to form a 2D image. Based on the 2D image, exposure can be calculated and adjusted using proper AET algorithms, but it is difficult to find the optimal window size, speed of target and capturing speed. In addition, if the texture or lighting s drastically, the imaging system may lose a few frames to find new exposure time to cope with new conditions. This is a critical issue in high-speed, sensitive security applications where losing a few frames creates a glitch in inspection and can be very expensive. More details can be found in Section 3. For most image processing applications, the signal to noise ratio in images is crucial for robust results. In addition, high quality images with good sharpness and contrast can significantly improve the post processing and image analysis results and reduce the processing time by eliminating time-consuming filters and complex image enhancement methods. In addition, images which are captured using appropriate exposure time, have a sense of balance in respect to the intensity level in image. Therefore, algorithms based on the threshold technique can perform reliably; e.g. the segmentation method in [1] that is based on the grey level threshold and also algorithm in [19]. In this paper, we propose a novel fuzzy logic approach to online exposure time calculation of line scan cameras. The aim is to implement a method that can calculate the exposure time in real time and also minimize the risk of glitch in the imaging system. This method is intended to be used in harsh industrial inspection where conditions unexpectedly. The rest of this paper is organized as follows. The next section describes some relevant work in the domain of exposure calculation. Section 3 presents the problem to be investigated. The proposed method is described in Section 4. Section 5 presents experimental results to demonstrate the feasibility and performance of the proposed approach, in comparison with two other methods. A brief conclusion is drawn in Section 6. 2 Relevant Work As mentioned before, majority of AET algorithms are for area cameras. The most common solution for exposure time calculation in line scan cameras is using fixed exposure time [2]. Another method is to buffer the lines and form a 2D image. Despite the difference between line scan and area cameras, the AET algorithms can be categorized into two main categories, histogram based and statistic based. In the histogram based approach, the aim is to extend the dynamic range of image. Many AET algorithms use histograms, but there are many limitations in using histograms. According to [3], it is very difficult to distinguish between back light and front light conditions [4][5]. Another approach which is widely used in exposure calculation is the fixed window segmentation method [5][6]. Kao et al. introduced a multiple exposure method in [7]. Although their algorithm aimed to improve the dynamic range of histogram, it was shown that their algorithm leaded to inconsistency [8]. Some histogram based algorithms divide an image into regions and calculate a local histogram for each region. The main drawback of the above algorithms is that it is assumed that there is only one main object of interest in the image and the object is in the middle of the image (or in a fixed position). If the object is very small in image or has different histogram characteristic than other objects in image, these algorithms will fail. The AET algorithms in the second category use statistic of an image to evaluate the quality of the image. In [8], lighting conditions were classified into three categories. The normal condition is where the difference between the mean of brightness in image and the median is not significant. If the image is too bright, then its mean is very different to median. Srinivasa et al. used a temporal dithering method for changing the brightness values [9]. However, their algorithm is not suitable for a dynamic environment [10]. 3 Problem Description Capturing high quality images in real time inspection systems is critical and a major prerequisite to most image processing algorithms [18]. Poor illumination or improper exposure time calculation is the main reason for losing

3 10 H. Farid Ghassem Nia, H. Hu, J. Q. Gan image details. The problem that we want to investigate comes from an industrial vision system where a line scan camera is employed to inspect a series of connected bars where each bar may be different in texture and color. The width of the bars is fixed but the length is unknown. The finished texture of bars is also unknown and can be reflective metal, plastic or even black matt wood. The system runs continuously so there may be no gap when the texture and materials. The speed of bar scanning is uneven (0.2 m/s to 1m/s) and is a function of other parameters. The imaging system can capture images of 1*35 millimeter which forms 1*350 pixels (a line). An advanced image processing method was developed to extract features from scanned images. The features may be as small as 1*1 millimeters. The illumination condition is stable and distance of camera to object is constant, as shown in Figure 1. Figure 1 Imaging system overview There are a few methods that have been suggested in literature, but all of them have limitations and drawback. The most common solution for exposure time calculation is using fixed window frame. The main disadvantage of this method is that a few frames may be lost in order to calculate new exposure time. This is because while the new exposure time is calculated, the image processing algorithm is fed with images that have been captured with old exposure time (poor images). Hence the image processing algorithm fails to function due to poor image quality. As a consequence, a part of target material will pass the inspection camera without reliable inspection and processing. This can be interpreted as a glitch in the imaging system, which is not permitted for sensitive security applications. Figure 2 shows the problem of glitch in the imaging system. A blue line (solid line) represents the average intensity for texture profile and a red line is the exposure time. Although the exposure unit and the intensity unit are different, we put them in one graph for comparison purposes only. Since the illumination is constant, in average intensity can be interpreted as the in properties of texture and reflectiveness of target surface. Figure 2 shows no particular pattern for intensity s. We used an exposure calculation algorithm that treats a line scan camera like an area camera in Figure 2. The lines are buffered to form an image with a length of 50 pixels (50 lines). It is assumed that our exposure calculation is ideal and can respond to s very quickly and needs only 1 frame to find proper exposure time. As can be seen in Figure 2, there are two areas in which the imaging system fails to capture high quality images and therefore image processing algorithms cannot detect satisfactory features. These two areas are marked as glitch A and glitch B. Figure 3 shows the effect of an ideal exposure calculation algorithm. The aim is to minimize the length of glitch as much as possible. This algorithm is able to respond to s on the fly and minimize the risk of failure. This paper presents a new method which is designed based on the characteristics of line scan cameras and can minimize the glitch in the imaging system and respond to s very quickly. In addition, the method can be executed in real time. Figure 2 The problem of glitch in inspection

4 Int. J. Modelling, Identification and Control, Vol. 21, No. 1, Figure 3 4 Proposed Method Effect of ideal exposure Exposure time calculation for real-time industrial inspection is a difficult task as an imaging system has many uncertainties. The imaging situation can rapidly and the rate of is unpredictable. An AET algorithm should be able to overcome these problems and provide the camera with desirable exposure time under any circumstances. Although there are many AI methods that can be employed to solve the above problems, using fuzzy logic controller is an appropriate approach to this problem, because in this experiment there is no complete dataset available. In addition, due to variance in the input (target material) it is impossible to collect a comprehensive dataset which covers all possible inputs. Fuzzy logic can provide a reliable solution when uncertainty is high [11]. In designing a fuzzy logic controller for exposure calculation, there are two main questions that need to be answered. 1) Is there any in target situation? 2) If yes, is there a need to the exposure? What is the optimum value for new exposure time? As mentioned in Section 2, AET algorithms can be cauterized into two categories. The first category uses the histogram of image and the second category uses the statistics of image. In this paper, we aim to draw a new framework which combines both categories. Due to the nature of the problem, answers to the above questions are not deterministic. Hence, we use fuzzy logic to cope with uncertainty in the system. In our experiment, the statistics of scanned lines and images were extracted and investigated to find variables or parameters that can be used as input to the fuzzy system. It was found out that there are two parameters that can be used as indicator and for estimating the new exposure time as well. The first parameter which is used as a measure of quality and also for calculating the new exposure is center of distribution of histogram. An ideal histogram distribution is in the form of Gaussian distribution where its center is the mid-range of intensity. According to the CCD sensor specifications [11], the best performance of a CCD sensor is when the intensity value is between 50% to 85%. Based on this fact, the centre of histogram distribution ideally should be in a range of 50% to 85%. In our line scan camera, the best sensitivity is between 40% and 60% of the range. Any deviation from the ideal value can be interpreted as reduction in image quality. The idea is about to stretch the histogram peak toward mid of histogram. In an 8 bit unsigned integer image, the ideal histogram distribution is in the form of Gaussian distribution where the centre (peak) of distribution is around 130. The histogram distribution can be a good indicator to assess image quality, but we need a method to understand if there is any in target material. The histogram distribution alone cannot be used as indicator of in target as it may reflects the pattern s in target. The challenge is that the variation in intensity or histogram can be due to local in pattern of material but not the in target. To overcome this problem we need a parameter which can distinguish between local in pattern and the in target material. With further investigation, it was noted that the median of scanned line values can be used as a parameter to assess the in target material. Therefore, the inputs to our fuzzy control system are median s and peak of histogram distribution which are shown in Figures 4 and 5, respectively. As illustrated in Figures 4 and 5, there are five membership functions for histogram peak, i.e., very dark, dark, normal, bright, and very bright. Three membership functions are also defined for median s. Figure 4 Figure 5 Table 1 No Change Moderate Huge Fuzzification of median s (Input fuzzy sets) Fuzzification of histogram peak (Input fuzzy sets) Look up table Very dark dark Normal Bright Small Large Large No Change No No Change Small Small Very Bright Small Large Large Based on the fuzzification of inputs and the look-up table as shown in Table 1, the defuzzification of output, i.e., exposure time, can be obtained based on the output fuzzy sets as shown in Figure 6. Based on the current state of the Copyright 2013 Inderscience Enterprises Ltd /IJMIC

5 12 H. Farid Ghassem Nia, H. Hu, J. Q. Gan system, it will increase or decrease the brightness level of the image as required. Figure 6 5 Experiments Defuzzification stage (Output fuzzy sets) To assess the performance of the proposed method and also to provide a platform for comparison, a simulator is developed using MATLAB under Windows 7 environment. The imaging model proposed by Nayar et al. [13] was adopted in the simulator. ( ) ( ) ( ) (1) The equation 1 shows image irradiance where, and are constants and show the strength of diffuse lobe, specular lube and specular spike, respectively [13]. The ratio between and shows surface bumpiness. This model takes the geometry and properties (e.g., reflectiveness) of surface into account. It also considers the angle and pose of camera, illumination, and target object. In our experiment, the properties of this imaging model were tailed to our application. Inputs to the simulator are real images captured from the actual hardware, but the sequence of reading and buffering data is simulated like a line scanner. For confidentiality reasons, we cannot show real images in this paper, but a manipulated sample image which has been blurred with noise is shown in Figure 7. The image was affected with Speckle and Poisson noise. It shows three different and distinct materials which pass under a line scanner camera with constant speed. This image was captured with fixed exposure time. We selected 397 scanned lines where each line contains 350 pixels (width). This image is intentionally selected to show the behaviour of the imaging system in transition between three distinct materials. The median values of these lines form a vector that represents the intensity profile of the image, as shown in Figure 8, and also reflects the local structure of the image in one dimension. As can be seen in Figures 7 and 8, the first part of the image was over exposed, the second part has normal intensity, and the third part is too dark. The first and third parts of the image suffer from poor quality and lack of useful information. To smooth the median vector for a better presentation, we applied Savitzky-Golay filter [14]. The result of filtering is shown in Figure 9. It should be noted that due to the nature of the imaging system, lines are scanned sequentially. Therefore, in practice, the filtering cannot be implemented on the full size image. Instead, a step by step approach can be used. We provided Figure 9 for illustration purposes only. Figure 8 Profile of intensity with fixed exposure time Figure 7 A sample image with three distinct materials under constant illumination and exposure time It is assumed that the pose and angle of illumination is constant. In addition, the distance between camera and target and aperture size are fixed. We also treated environment light as noise added to the image. It should be noted that for most digital cameras (either line scan or area ), there are at least three parameters that can be d on the fly: analogue gain, digital gain, and exposure time. Gains have direct relationship with exposure time and can be calculated using exposure time and camera characteristics. In this paper, for the sake of simplicity, we calculated the exposure time only and gains were assumed to be fixed. Figure 9 Effect of filtering on intensity profile To evaluate the proposed method, two traditional exposure time calculation methods, such as fixed exposure time technique and window-based method, were also

6 A novel fuzzy logic approach to online exposure time calculation of line scan cameras in industrial inspection 13 implemented. Figure 8 shows the profile of intensity while the fixed exposure time technique was used. The standard deviation of the intensity profile is 75.51, which illustrates huge variation in the image. It can be seen that the first 50 millimeters of the image is saturated and median of intensity is around 250. On the other hand, after 160 millimeters median of intensity is less than 50, which is too dark. The only part with satisfactory intensity level is in the middle of the image. It is clear from Figure 8 that the fixed exposure time method is not efficient as it is unable to react to s in the target object to obtain high quality images. The second technique widely used is window based. In window based method, a window with fixed size (e.g. 100*100 pixels) is used as a frame to form a 2D image using scanned lines. Each window frame is treated as a single 2D image and suitable exposure time is calculated using the relevant method and applied to the next frame. In ideal case, the exposure time calculation algorithm needs one or two frames to estimate exposure time. Figure 10 Profile of intensity with a standard exposure time calculation method Figure 11 Profile of intensity with the proposed fuzzy exposure time calculation

7 Int. J. Modelling, Identification and Control, Vol. 21, No. 1, With the same materials as shown in Figure 7, the captured image using the window-based technique (window size is 100 mm here) has the median of intensity profile as shown in Figure 10. The standard deviation of the intensity is 22.61, which is a great improvement in comparison with the fixed exposure time method. In Figure 10, the exposure time for first 100 millimeter is approximately 130 milliseconds. Although this exposure time is suitable for the first material, it is not suitable for the second material. By taking a closer look into the diagram, it can be noted that after 60 millimeters, the material s. The AET algorithm is not able to detect this quickly as it should wait to scan and buffer 100 millimeters (lines) and to estimate the exposure time for the next frame. After 100 millimeters, the correct exposure time was calculated and applied to the camera. However, the image quality is poor between 60 to 100 millimeters (glitch). This can be seen as low intensity profile in Figure 10. Similar situations happened when the third material passed under the line scan camera. It is clear that the window size should be big enough to cover features of interest. Meanwhile if the size is too big then AET algorithm is not able to react to s promptly. In addition, most AET calculation algorithms need a suitable size to analyse and calculate the exposure time. The suitable size here can be interpreted as one that can contain minimum needed information regarding the region of interest (ROI) which is essential for the AET algorithm. This size depends on the size of ROI. The fixed window size method may work better with ROI, but in line scan cameras with continuous flow of data, it is difficult to define a region of interest. Another disadvantage of this method is the computational costs as it needs to read, buffer and analyse images continuously. As can be seen in Figure 10, two parts of the image were captured with improper exposure time. In a similar way, the intensity profile produced by the proposed method is shown in Figure 11. The standard deviation for the intensity is 17.61, which is a significant improvement compared to the two traditional methods. The proposed method is able to react to s very fast. In addition, only a few millimeters after s (which are necessary to figure out the new exposure time) the proposed method could assign new suitable exposure time. The mean of intensity is which is an ideal intensity level. In addition, the mean of intensity for three distinct materials is very close to the mean of the whole image, which means that most parts of the image have good quality. Table 2 Statistical comparison Fixed exposure Fixed window Fuzzy exposure Mean Standard deviation Extreme Glitch N/A 40(mm) 6(mm) Table 2 gives the statistical performance data extracted from intensity profiles, for comparing the fixed exposure method, the fixed window (window based) method and the proposed fuzzy exposure method. The three methods have similar intensity means. The proposed method has the smallest standard deviation in intensity, which demonstrates stability and robustness. In addition, the proposed fuzzy method produced the smallest glitch that is about 6 millimeters, a significant improvement compared to 40 millimeters for the window based method. To examine the behaviour of the proposed algorithm under different circumstances, a number of different materials were used to carry out further test. The textures of the materials were categorized into three categories, dark, grey and white. The aim was to assess the consistency of the proposed algorithm when various combinations of materials pass under the camera, e.g., transition from dark to white materials or vice versa. Transition from dark or white to grey is identified as moderate in texture and transition from dark to white (or white to dark) is treated as extreme in texture. This experiment was conducted in two phases. In the first phase, repeatedly two materials with different texture categories pass under the camera, including both extreme texture s and moderate texture s. The result of this experiment is shown in Table 3. The small standard deviation of intensity shows that the proposed AET algorithm has reliable performance. The size of glitch is also smaller than 5 millimeters which illustrates the usefulness of the proposed algorithm. In the second phase of the experiment, similar tests were carried out using three different materials. The small standard deviation and small size of glitch reconfirmed the previous findings. Table 3 Experimental results on a set of materials STD deviation Max glitch 2- materials, moderate 2- materials, extreme 3- materials, moderate 3- materials, extreme (mm) 4.8(mm) 5.1(mm) 6.9(mm) The performance of the proposed method regarding real time execution was also investigated and it was concluded that the proposed method can be executed in parallel to image processing algorithm and in real time (with specified deadline). The capability of real time execution is critical for high speed systems. To investigate on the behaviour of the proposed algorithm when facing the variation of patterns in materials, a test was conducted using one material with maximum contrast in its pattern (zebra like pattern). The results showed that the standard deviation for average intensity is which is due to huge variation in the material pattern. However, the glitch size was smaller than 3 millimetres. This experiment shows that fuzzy exposure time calculation can adjust the exposure time when there is huge variation of patterns. Copyright 2013 Inderscience Enterprises Ltd /IJMIC

8 A novel fuzzy logic approach to online exposure time calculation of line scan cameras in industrial inspection 15 Another approach to the problem of exposure time calculation in varying materials is to control illumination [7][15][16][17]. In theory, illumination control can have similar effect on image quality as exposure time control, but our experiments show that there are many limitations and complications in the illumination control that needs extra hardware to control the light. To assess the proposed method further, we compared our method with the method proposed by Deng et al. [15] where bright field diffused illumination was used. The aim of this comparison was to understand how fast the imaging system can react when a occurs in the target material. In this test, the performance measure was the rate of feature detection as it was assumed that if the image has high quality then image processing algorithm can detect the features reliably and without failure. The feature detection rate achieved by the proposed method for 3 different consecutive materials (with great difference in their properties) was 100%, which was decreased to 98.3 when using the window based method and illumination technique in [15]. In addition, there are several limitations in the illumination control methods [15][16]. Each illumination technique is designed to highlight a certain type of features in target material [17]. If feature properties, then another illumination technique should be employed. 6 Conclusions A novel fuzzy logic control based approach is proposed in this paper to overcome the problem in exposure time calculation for line scan cameras used in harsh industrial inspections so that automatic exposure time calculation in sensitive industrial inspection can be achieved. The exposure time is calculated on the fly with minimum data lost, when the target object s rapidly. The proposed algorithm was tested with numerous materials with different combinations and compared with two traditional methods. The results have shown that the proposed fuzzy exposure calculation method has consistent and robust performance. The proposed method was also compared to illumination control technique and it was concluded that the fuzzy exposure calculation is superior to illumination control for sensitive inspection. The test was carried out many times with different inputs and satisfactory results were obtained. Acknowledgments: This research has been financially supported by a Knowledge Transfer Partnership funded by Technology Strategy Board and Vaccumatic Limited in Colchester, Grant No. KTP The authors would like to thanks David Long & Rob Greenwood for their valuable advices and their contribution in this project. References [1] Y. Jiang, Z. Hao, G. Yuan, and Z. Yang, Multilevel thresholding for image segmentation through Bayesian particle swarm optimization, Int. 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