Identification of Noisy Poultry Portion Images Using a Neural Network

Transcription

1 Identification of Noisy Poultry Portion Images Using a Neural Network ADNAN KHASHMAN, GULSUM Y. ASIKSOY, Intelligent Systems Research Group (ISRG) Department of Electrical & Electronic Engineering Near East University Near East Boulevard, Nicosia N. CYPRUS { amk, gyildiz}@neu.edu.tr Abstract: - The automatic sorting of products in food processing plants has become more important as faster processing systems emerge continuously. In raw poultry processing plants, popular chicken portions such as breasts, drumsticks, fillet, legs, and wings have to be sorted prior to packaging. Some current sorting methods use the portion weight as an indicator, however varying sizes of the different portions could result in similar weight amongst the different portions; thus causing incorrect identification. One solution is using portion images and an intelligent classifier to identify the portions, therefore, simulating the way human laborers sort the portion in a production line. The problem with the intelligent imaging method is the potential noise on images which could occur from different sources such as hardware or software. In this paper, we present a raw chicken portion identification system that uses a database of noisy and noise-free images to train a neural network to identify the different chicken portions. Experimental results demonstrate the robustness of the proposed identification system, and suggest that it can be effectively used with high accuracy in a poultry processing plant. Key-Words: - Automatic Sorting, Chicken Portions, Rotational Invariance, Neural Networks, Image Processing Introduction The sorting of a produce in food processing plants is of great importance, where different products are sorted or separated into designated containers prior to packaging and then distribution. In this work, we investigate a way to automate the sorting process of raw poultry products; and focus in particular on sorting raw chicken portions. Poultry meat in general and chicken meat in particular has become the world s second most consumed type of meat []. In modern poultry processing plants [],[3],[4], the slaughter of birds, and then cutting up the carcass is fully automated without the handling of human operators. This is important as it reduces the potential of contamination from human operators to the raw poultry carcasses and vice-versa [5]. The next process after cutting up the carcass is to separate or sort the portions into different containers, followed up by packaging. Few plants use an automated method based on the weight of a portion as an indicator for sorting [],[6], but manual sorting continues to be the most prevalent method used for sorting poultry portions prior to packaging. However, there are problems inherent with such manual sorting method including inconsistency, high labor costs worker fatigue, and increasing employment costs; these have all been identified as the important factors driving the demand for automation of the industry [7]. Moreover, we have become more cautious about handling raw meat and poultry products since the spread of diseases such as the bird flu. Therefore, finding a method to sort the different raw poultry portions without the physical handling and contact by human operators is of utmost importance [8]. The use of artificial intelligent systems such as neural networks to improve food production, processing, and quality in general, and poultry processing in particular has accelerated recently [9]. For example, the work in [0] demonstrated a multispectral imaging system for food safety inspection of poultry carcasses. In [], a hyperspectral imaging system demonstrated potential to detect surface fecal and ingesta contaminants on poultry carcasses. In [], a technique based on two narrow-band color-mixing was suggested for identification of broiler chicken carcass conditions with focus on the chicken breast area. In [3], a poultry skin tumor detection method ISSN: ISBN:

2 using hyperspectral reflectance imaging data was also proposed. Most recently, our work in [4] presented an intelligent system for poultry portions. This system, which yielded an overall correct identification rate of 9.67%, used a back propagation neural network which was trained using images of 30 o -rotated poultry portions. In this paper we continue our work at addressing the problem of potential contamination due to human laborers physical handling of raw poultry during the sorting process, and aim to improve the system we proposed in [4] by accounting for noisy portion images and achieving higher correct identification rates. In this work we use local pattern averaging during the image processing phase and a back propagation neural network classifier to identify the raw poultry portions. This identification system is the main part of a fully automated system, once the portions are correctly identified, they can then be physically separated by a robotic arm which is controlled by the output of the neural network in the identification system. In our experiments we use a raw chicken and its portions (breast, drumstick, fillet, leg, thigh, and wing) as the objects within our image database. These chicken portions are considered due to their popularity amongst customers in general. The rotational invariance property of our proposed intelligent system is a result of obtaining numerous images of the different portions at different orientation; here all parts are rotated by 5 o degrees and their images are captured. In our previous work [4], the image database was built using images of rotated portions at 30 o degrees; we suggest that reducing the rotation angle should increase the number of images in the database and improve the accuracy rate. The neural network acts as the identifier of the captured images, and its out can be used for further processing such as moving mechanical arms to physically separate the different portions into separate containers. Furthermore, we enlarge the raw portion image database by using portion images with added Gaussian noise of two amounts (5% and 0 % noise). The additional noisy images create a challenge to the trained neural network and test its capability in identifying portions in distorted images. The source of such distortion in a processing plant can be from image grabbing hardware or software errors. We organize this paper as follows: Section presents the chicken portion image database. Section 3 describes the portion identification system and reviews the image processing phase, as well as the neural network training and decision phase. Section 4 presents the system implementation results. Finally, Section 5 concludes the work that is presented within this paper. Building the Image Database When training a neural network, the use of a sufficient database is of utmost importance. In this work we use noise-free and noisy images of raw chicken portions as the database. The noise-free images in our database were of portions of a raw medium-size chicken which we manually cut into the most commonly preferred portions; namely, breast, drumstick, fillet, leg, thigh, and wing. Figure shows examples of the noisy and noise-free images of the six portions. When grabbing the original portion images, chicken portions were placed against a white background, and their distance from the camera lens was fixed at 70cm. Each portion s obverse (front) side and reverse (back) side were captured in a. b. c. d. e. f. Original 5% Noise 0% Noise Fig. Examples of poultry portions: (a) breast (b) drumstick (c) fillet (d) leg (e) thigh (f) wing. ISSN: ISBN:

3 consideration of the possibility that in a processing plant a portion may land on a conveyor belt with its obverse or reverse side facing the camera. As one of our goals is to assure the rotational invariance property of our system, several images of the different portions were obtained with each portion being rotated by intervals of 5 o, thus, resulting in 48 images of each of the six portions (4 obverse side and 4 reverse side). This provides us in total with 88 poultry portion noise-free images. Gaussian noise of 5% and 0% was then added to the original noise-free images, thus resulting in additional 88 5%-noisy images and 88 0%-noisy images. In total our database contains 864 chicken portion images. Figure shows an example of a chicken portion s noise-free images at 5 o rotations. Captured noise-free images of the six chicken portions at 0 o, 45 o, 90 o, 35 o, 80 o, 5 o, 70 o, and 35 o degrees rotations will be used for training the back propagation neural network, thus, providing 96 images for training (8 obverse and 8 reverse sides of each of the six portions). The remaining 9 noisefree portion images, as well as the 88 5%-noisy images and the 88 0%-noisy images will be used for testing the trained neural network within the identification system. The method of rotating the chicken portions by 5 o intervals is considered more efficient than using 30 o as used in our previous work [4]; as is exposes the neural network during training to more potential orientations of the chicken portions. Furthermore, the required identification task in this work is much more challenging to the neural network, as it will be tested using 768 images when trained with 96 images only. Figure 3 shows examples of the chicken portion training images. 3 The Identification System The implementation of the poultry portion identification system consists of two phases: an image processing phase where chicken portion images undergo resizing, color conversion, and pattern averaging in preparation to be presented to the second phase; which is training a back propagation neural network to identify the different portions. 3. Image Processing Phase The first phase of the proposed identification system involves preparing the training/testing image data for the neural network. Care must be taken in order to provide the neural network with sufficient data representations of the rotated poultry portions if we are to achieve meaningful learning, while attempting to keep the computational costs to a minimum. Image processing is carried out in this phase, where images of the rotated chicken portions are captured in RGB color with the dimensions of 59x944 pixels. The images are resized to 00x00 pixels and converted to grayscale, which consists of pixel values between 0 and 55 using the Adobe Photoshop 7.0 Element software tool. Pattern averaging is then applied to the gray 00x00 pixel images. Here, the image is segmented using 5x5 kernels and the pixel values within each kernel are averaged and saved as feature vectors for training the neural network. This method results in a 0x0 fuzzy bitmap that represents the different chicken portions at various rotations. Other segment sizes can also be used, however, the larger the segment size is, the higher the computational cost will be. 0 o 5 o 30 o 45 o 60 o 75 o 90 o 05 o 0 o 35 o 50 o 65 o 80 o 95 o 0 o 5 o 40 o 55 o 70 o 85 o 300 o 35 o 330 o 345 o Fig. Rotations by 5 o of a chicken leg reverse side. ISSN: ISBN:

4 Drumstick 0 o Leg 45 o Breast 90 o Thigh 35 o Fillet 80 o Wing 5 o Leg 70 o Breast 35 o Fig. 3 Examples of chicken portions training images. A 5x5 segment size results in 0x0 feature vector bitmap, thus requiring 400 neurons in the neural network input layer. The averaging of the segments within an image reduces the amount of data required for neural network implementation thus providing a faster identification system. Pattern averaging can be defined as follows: PatAv i = s s k l sl sk p l = k = i ( k, l) () where k and l are segment coordinates in the x and y directions respectively, i is the segment number, S k and S l are segment width and height respectively, P i (k,l) is pixel value at coordinates k and l in segment i, PatAv i is the average value of pattern in segment i that is presented to neural network input layer neuron i. The number of segments in each image of size XY pixels (X = Y = 00) containing a poultry portion, as well as the number of neurons in the input layer is i where i = (,, 3,, n), and: = X s Y k s l n () Previous works in [4],[5] using this preprocessing method showed sufficient representation of the objects within the images and meaningful data within the averaged patterns were obtained to aid the neural network learning and classification. Pattern averaging provides meaningful learning and marginally reduces the processing time. For the work presented within this paper, pattern averaging overcomes the problem of varying pixel values within the segments as a result of rotation, thus, providing a rotation invariant system. Using a segment size of 5x5 pixels, results in a 0x0 bitmap of averaged pixel values that will be used as the input for the second phase which is the neural network training and testing. 3. Neural Network Training Phase The second phase of the proposed identification system is the implementation of a back propagation neural network classifier. This phase consists of training the neural network using the averaged patterns (feature vectors) obtained from the first phase. Once the network converges, this phase will only comprise generalizing the trained neural network using one forward pass. A three-layer feed forward neural network with 400 input neurons, 9 hidden neurons and 6 output neurons is used to identify the chicken portions and classify them into: breast, drumstick, fillet, leg, thigh, or wing. The number of neurons in the input layer is dictated by the number of averaged segments in the 0x0 bitmap. The choice of 9 neurons in the hidden layer was a result of various training experiments using lower and higher hidden neuron values. The chosen number assured meaningful training while keeping the time cost to a minimum. The six neurons in the output layer represent the six chicken portions. The activation function used for the neurons in the hidden and output layers is the sigmoid function. During the learning phase, initial random weights of values between 0.3 and 0.3 were used. The learning rate and the momentum rate were adjusted during various experiments in order to achieve the required minimum error value of 0.005; which was considered as sufficient for this application. Figure 4 shows the topology of the neural network. The neural network is trained using only 96 chicken portion images out of the available 864 images. The 96 training images are noise-free and contain rotated portions at 45 o intervals as shown in examples in Figure 3. The remaining 768 portion images contain: 9 noise-free, 88 5%-noisy, and 0%-noisy images; these are the testing images which are not exposed to the network during training and shall be used to test the robustness of the trained neural network in identifying the portions. ISSN: ISBN:

5 Gray input image 59x944 pixels Resized image 00x00 pixels Chicken Portion Pattern Fig. 4 The Chicken portion identification neural network topology Input Layer 9 Hidden Layer Output Layer Breast Drumstick Fillet Leg Thigh Wing 4 Experimental Results The simulation of the proposed system and the experimental results were obtained using a.8 GHz PC with GB of RAM, Windows XP OS and Borland C++ compiler. Table lists the final parameters of the successfully trained neural network, which learnt and converged after 445 iterations and within 53.5 seconds. The running time for the neural network after training and using one forward pass was 6.x0-4 seconds; this running time is the time required for the trained neural network to identify one portion. The implementation results of the trained identification system were as follows: using the training image set (96 images) yielded 00% correct identification as would be expected. The system s correct identification rates using the chicken portion testing images were as follows: Noise-free images (9): out of 9 testing noise-free images, 85 were correctly identified yielding 96.35% correct identification. 5%-Noisy images (88): out of 480 testing images (9 noise free %-noisy), 466 images were correctly identified yielding 97.08% correct identification. 0%-Noisy images (88): out of 768 testing images (9 noise free %-noisy %-noisy), 747 images were correctly identified yielding 97.7% correct identification The obtained identification rates (see Table ), demonstrate the robustness and efficiency of the proposed poultry portion identification system. Table. Neural network final parameters and correct identification rates (CIR). Input Neurons 400 Hidden Neurons 9 Output Neurons 6 Learning Coefficient Momentum rate 0.3 Minimum Error Iterations 445 Training time (seconds) 53.5 Run time (seconds) 6. x 0-4 Table. The neural network based system s correct identification rates. Chicken Portion Database Set Noise-Free With 5% Noise With 0% Noise Training Images (96/96) 00% (96/96) 00% (96/96) 00% Testing images (85/9) 96.35% (466/480) 97.08% (747/768) 97.7% 5 Conclusion This paper presented a rotation-invariant neural network based poultry portion identification system. The system training and testing uses noise-free images of six chicken portions (breast, drumstick, fillet, leg, thigh, and wing) that were rotated at intervals of 5 o degrees, as well as portion images of Gaussian noise addition of two amounts, 5% and ISSN: ISBN:

6 0% noise. Training the neural network within the identification system uses only noise-free images containing chicken portions at 45 o rotations. Testing the trained neural network was performed using the remaining portion noise-free and noisy images. In summary, out of the total 864 portion images in our database, 96 images were used for training, and 768 images for testing the system s capability in identifying the portions. The highest obtained rate for correct identification of testing set portion images was a remarkable 97.7%. These images were not presented to the neural network during training. The speed of the proposed system indicates that the system can be implemented successfully in a real life application; e.g. in poultry processing plants. The training time for the neural network was approximately 54 seconds; this is reasonable since training is only performed once prior to using or running the system. The running time for a trained system; i.e. the time taken to recognize a chicken portion, was a fast 6.x0-4 seconds. The successful implementation of our proposed system has been shown throughout the high correct identification ratios, and the fast execution time for the trained system. Therefore, it can be suggested that such an automated poultry portion identification system, can be implemented in practice, and would form the first part of a fully automated poultry portion sorting system. The importance of such an automated system arises from the need to completely prevent human-raw poultry physical contact in order to avoid potential contamination. References: [] D. Somsen, A. Capelle, and J. Tramper, Production Yield Analysis in the Poultry Processing Industry. Journal of Food Engineering, Vol. 65, 004, pp [] A.R. Sams, Poultry Meat Processing, CRC Press, 00. [3] G.J. Van Hoogen, Poultry Processing: Developing new Tools to be Competitive, XVII th European Symposium on the Quality of Poultry Meat, 005, pp [4] Gainco Inc., Accessed online: June 009. [5] K.M. Keener, M.P. Bashor, P.A. Curtis, B.W. Sheldon, and S. Kathariou, Comprehensive Review of Campylobacter and Poultry Proc., Comprehensive Reviews in Food Science and Food Safety, Vol. 3, 004, pp [6] Gainco Inc., In-Motion Portion Sizing and Distribution Equipment, GS-500, Accessed online: June 009. [7] B. Jarimopas and N. Jaisin, An Experimental Machine Vision System for Sorting Sweet Tamarind, Journal of Food Engineering, Vol. 89, 008, pp [8] A. Bardic, Poultry in Motion: Demand for Labor-Saving Automated Poultry Processing Equipment Escalates, National Provisioner, 004, pp. -8. [9] C.J. Du and D.W. Sun, Learning Techniques used in Computer Vision for Food Quality Evaluation: a Review, Journal of Food Engineering, Vol. 7, 006, pp [0] C.C. Yang, K. Chao, and Y.R. Chen, Development of Multispectral Image Processing Algorithms for Identification of Wholesome, Septicemic, and Inflammatory Process Chickens, Journal of Food Engineering, Vol. 69, 005, pp [] B. Park, K.C. Lawrence, W.R. Windham, and D.P. Smith, Performance of Hyperspectral Imaging System for Poultry Surface Fecal Contaminant Detection, Journal of Food Engineering, Vol. 75, 006, pp [] K. Chao, Y.R. Chen, F. Ding, C.C. Yang, and D.E. Chan, Development of Two-Band Color- Mixing Technique for Identification of Broiler Carcass Conditions, Journal of Food Engineering, Vol. 80, 007, pp [3] S. Nakariyakul and D.P. Casasent, Fast Feature Selection Algorithm for Poultry Skin Tumor Detection in Hyperspectral Data, Journal of Food Engineering, Vol. 94, 009, pp [4] A. Khashman, G. Asiksoy, and H. Fikretler, Intelligent Portion Identification System for Poultry Processing Plant, In D. Lepadatescu, N.E. Mastorakis, and A. Khashman (Eds.), Advances in Manufacturing Engineering, Quality and Production Systems, Vol., 009, pp [5] A. Khashman, Intelligent Local Face Recognition,. In K. Delac, M. Grgic, M. Stewart Bartlett (Eds.), Recent Advances in Face Recognition, Ch. 5, IN-TECH, Vienna, Austria, December 008. ISSN: ISBN: