Algorithms for classification of objects in images of the receiving camera of a modern reverse vending machines
Abstract
The work examines the problem of developing mathematical and software for image analysis in modern reverse vending machines. As part of the approach based on the use of shallow machine learning methods, the problem of processing images of objects subject to raw material processing in reverse vending machines is formulated as a task of segmenting these images, followed by subsequent classification by shape. A review and comparison of known segmentation methods is carried out in order to extract the shape of objects and generate features for classification. As a result of the comparison, a choice is made in favor of the active contours method. A relatively simple algorithm for classifying segmented objects based on “random forest” is proposed and studied. We also propose two algorithms for extracting classification features: an algorithm based on the analysis of the degree of fullness of parts of a segmented image and an algorithm that calculates the properties of the object area. As an alternative approach, a method for classifying the resulting images as a whole, without preliminary feature selection, based on deep learning, is also described. The problem of shortage of training data is discussed, and possible approaches to solving it are given. We describe the implementation of a convolutional neural network — a classifier with the DenseNet architecture, obtained using the transfer learning technique. The results of experiments to evaluate the effectiveness of the considered algorithms, conducted on the provided training set, are presented. The selected segmentation method, as well as both considered classifiers, demonstrated a high level of efficiency. When comparing the results of classification algorithms based on shallow (“random forest”) and deep machine learning (convolutional neural network), a choice was made in favor of the neural network approach when certain conditions were met for the training data.
Downloads
References
2. Akhmetzyanov K. R. and Yuzhakov A. A. (2018) Sravnenie svertochnykh neironnykh setei dlya zadach sortirovki musornykh otkhodov [Comparison of convolutional neural networks for waste sorting tasks]. Izvestiya SPbGETU LETI. (6). P. 27–32. (in Russian)
3. González R. C., Woods R. E. and Masters B. R. (2009). Digital Image Processing, Third Edition. Journal of Biomedical Optics. 14. 029901.
4. Chow C. K. and Kaneko T. (1972). Automatic boundary detection of the left ventricle from cineangiograms. Computers and biomedical research, an international journal. 5(4). P. 388–410. DOI
5. Canny J. (1986). A computational approach to edge detection. IEEE transactions on pattern analysis and machine intelligence. 8(6). P. 679– 698.
6. Fu K. and Mui J. K. A survey on image segmentation. Pattern Recognit. 1981. P. 3–16.
7. Hojjatoleslami S. A. and Kittler J. (1998). Region growing: a new approach. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 7(7). P. 1079–1084. DOI
8. Jianbo Shi and Malik J. (2000). Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 22(8). P. 888–905. doi:10.1109/34.868688" target="_blank">DOI
9. Kass M., Witkin A. and Terzopoulos D. (1988) Snakes: Active contour models. Int J Comput Vision 1, P. 321–331. DOI
10. Breiman L. (1984). Classification and Regression Trees (1st ed.). Routledge. DOI
11. Louppe G. (2014). Understanding Random Forests: From Theory to Practice. arXiv: Machine Learning.
12. Shorten C. and Khoshgoftaar T. M. (2019). A survey on Image Data Augmentation for Deep Learning. Journal of Big Data. 6. P. 1–48.
13. Weiss K. R., Khoshgoftaar T. M. and Wang D. (2016). A survey of transfer learning. Journal of Big Data, 3.
14. Huang G., Liu Z., Van Der Maaten L. and Weinberger K. Q. (2017) Densely Connected Convolutional Networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA. P. 2261–2269. DOI
15. Uijlings J. R., Sande K. E., Gevers T. and Smeulders A. W. (2013). Selective Search for Object Recognition. International Journal of Computer Vision. 104. P. 154–171.
16. Dalal N. and Triggs B. (2005) Histograms of oriented gradients for human detection (2005). IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), San Diego, CA, USA. vol. 1. P. 886–893. DOI
17. González R. C., Woods R .E. and Eddins S. L. (2006). Digital image processing using MATLAB.
18. Felzenszwalb P. F., Girshick R. B., McAllester D. and Ramanan D. (2010). Object detection with discriminatively trained part-based models. IEEE transactions on pattern analysis and machine intelligence. 32(9). P. 1627–1645. DOI
19. OpenCV command. (2015) OpenCV documentation index URL
Условия передачи авторских прав in English
