https://doi.org/10.4081/jae.2024.1564
Multi-class segmentation of navel orange surface defects based on improved DeepLabv3+
Published: 20 February 2024
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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To address the problems of current mainstream semantic segmentation network such as rough edge segmentation of navel oranges defects, poor accuracy of small target defect segmentation and insufficient deep-level semantic extraction of defects, feature information will be lost, a multi-class segmentation model based on improved DeepLabv3+ is proposed to detect the surface defects of navel oranges. The Coordinate Attention Mechanism is embedded into the DeepLabv3+ network for better semantic segmentation performance, while the dilated convolution of Atrous Spatial Pyramid Pooling structure is replaced with deformable empty convolution to improve the fitting ability of the network to target shape changes and irregular defects. In addition, a BiFPN-based feature fusion branch is introduced at the DeepLabv3+ encoder side to realize multi-scale feature fusion and enrich feature space and semantic information. The experimental results show that the average intersection ratio and average pixel intersection ratio accuracies of the improved DeepLabv3+ model on the navel orange surface defect dataset are 77.32% and 86.38%, which are 3.81% and 5.29% higher than the original DeepLabv3+ network, respectively, improving the extraction capability of navel orange defect features and having better segmentation performance.
Badrinarayanan, V., Kendall, A., and Cipolla, R. 2017. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE T PATTERN ANAL. 39(12): 2481-2495.
DOI: https://doi.org/10.1109/TPAMI.2016.2644615
Bhargava, A., Bansal, A. Automatic Detection and Grading of Multiple Fruits by Machine Learning. 2020. Food Anal. Methods 13. 751–761.
DOI: https://doi.org/10.1007/s12161-019-01690-6
Chen, S., Song, Y., Su, J., Fang, Y., Shen, L., Mi, Z. and Su, B. 2021. Segmentation of field grape bunches via an improved pyramid scene parsing network. Int J Agr Biol Eng. 14(6):185-194.
DOI: https://doi.org/10.25165/j.ijabe.20211406.6903
Chen, L. C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. 2018. Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proceedings of the European conference on computer vision (ECCV) (pp. 801-818).
DOI: https://doi.org/10.1007/978-3-030-01234-2_49
Everingham, M., Eslami, S. A., Van Gool, L., Williams, C. K., Winn, J., and Zisserman, A. 2015. The pascal visual object classes challenge: A retrospective. INT J COMPUT VISION. 111:98-136.
DOI: https://doi.org/10.1007/s11263-014-0733-5
Fan, S., Li, J., Zhang, Y., Tian, X., Wang, Q., He, X., Zhang, C., and Huang, W. 2020. On line detection of defective apples using computer vision system combined with deep learning methods. J FOOD ENG. 286:110102.
DOI: https://doi.org/10.1016/j.jfoodeng.2020.110102
Hou, Q., Zhou, D., and Feng, J. 2021. Coordinate attention for efficient mobile network design. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 13713-13722).
DOI: https://doi.org/10.1109/CVPR46437.2021.01350
Hu, J., Shen, L., and Sun, G. 2018. Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7132-7141). Jin, L. I., Renyong, Z. H. A. O., Boxue, D. U., Liucheng, H., and Kai, B. 2021. Research Progress of Nondestructive Detection Methods for Defects of Electrical Epoxy Insulators. Transactions of China Electrotechnical Society. 36(21):4598-4607.
Li J. B., Huang W. Q., and Zhao C. J. 2015. Machine vision technology for detecting the external defects of fruits—A review. The Imaging Science Journal. 63(5): 241-251.
DOI: https://doi.org/10.1179/1743131X14Y.0000000088
Liang, X., Jia, X., Huang, W., He, X., Li, L., Fan, S., Li, J., Zhao, C., and Zhang, C. 2022. Real-Time Grading of Defect Apples Using Semantic Segmentation Combination with a Pruned YOLO V4 Network. Foods. 11(19):3150.
DOI: https://doi.org/10.3390/foods11193150
Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. 2017. Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2117-2125).
DOI: https://doi.org/10.1109/CVPR.2017.106
Long, J., Shelhamer, E., and Darrell, T. 2015. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3431-3440).
DOI: https://doi.org/10.1109/CVPR.2015.7298965
Nithya, R., Santhi, B., Manikandan, R., Rahimi, M., and Gandomi, A. H. 2022. Computer Vision System for Mango Fruit Defect Detection Using Deep Convolutional Neural Network. Foods. 11(21):3483.
DOI: https://doi.org/10.3390/foods11213483
Raman S., Chougule A., and Chamola V. 2022. A low power consumption mobile based IoT framework for real-time classification and segmentation for apple disease. Microprocess. Microsyst. 94:104656.
DOI: https://doi.org/10.1016/j.micpro.2022.104656
Ren, H.-E., Bai, J.-Y. 2013. Color Grading Based on Dynamic Clustering in Lab Color Space. Jisuanji Gongcheng/ Computer Engineering, 39(6).
Rong, D., Rao, X., and Ying, Y. 2017. Computer vision detection of surface defect on oranges by means of a sliding comparison window local segmentation algorithm. COMPUT ELECTRON AGR. 137:59-68.
DOI: https://doi.org/10.1016/j.compag.2017.02.027
Ronneberger, O., Fischer, P., and Brox, T. 2015. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18 (pp. 234-241). Springer International Publishing.
DOI: https://doi.org/10.1007/978-3-319-24574-4_28
Soltani Firouz, M., and Sardari, H. 2022. Defect Detection in Fruit and Vegetables by Using Machine Vision Systems and Image Processing. FOOD ENG REV. 14(3):353-379.
DOI: https://doi.org/10.1007/s12393-022-09307-1
Strudel, R., Garcia, R., Laptev, I., and Schmid, C. 2021. Segmenter: Transformer for semantic segmentation. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 7262-7272).
DOI: https://doi.org/10.1109/ICCV48922.2021.00717
Sun, X., Li, G., and Xu, S. 2020. Fastidious Attention Network for Navel Orange Segmentation. arXiv preprint arXiv:2003.11734.
Tang, Y., Bai, H., Sun, L., Wang, Y., Hou, J., Huo, Y., and Min, R. 2022. Multi-Band-Image Based Detection of Apple Surface Defect Using Machine Vision and Deep Learning. Horticulturae. 8(7):666.
DOI: https://doi.org/10.3390/horticulturae8070666
Tian, K., Zeng, J., Song, T., Li, Z., Evans, A. and Li, J. 2022. Tomato leaf diseases recognition based on deep convolutional neural networks. J. Agric. Eng. Res. 54(1).
DOI: https://doi.org/10.4081/jae.2022.1432
Unay, D. 2022. Deep learning based automatic grading of bi-colored apples using multispectral images. MULTIMED TOOLS APPL. 81(27):38237-38252.
DOI: https://doi.org/10.1007/s11042-022-12230-6
Woo, S., Park, J., Lee, J. Y., and Kweon, I. S. 2018. Cbam: Convolutional block attention module. In Proceedings of the European conference on computer vision (ECCV) (pp. 3-19).
DOI: https://doi.org/10.1007/978-3-030-01234-2_1
Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M. and Luo, P. 2021. SegFormer: Simple and efficient design for semantic segmentation with transformers. ADV NEUR IN. 34:12077-12090.
Xie, X., Ge, S., Xie, M., Hu, F., Jiang, N., Cai, T., and Li, B. 2018. Image matching algorithm of defects on navel orange surface based on compressed sensing. J AMB INTEL HUM COMP, 1-9.
DOI: https://doi.org/10.1007/s12652-018-0833-0
Yang, G. L., Luo, L., Feng, Y. Q., and Zhao, H. S. 2014. Research of Navel Orange Defect and Color Detection Based on Machine Vision. APPL MECH MATER. 513:3442-3445. Trans Tech Publications Ltd.
DOI: https://doi.org/10.4028/www.scientific.net/AMM.513-517.3442
Yao, J., Qi, J., Zhang, J., Shao, H., Yang, J., and Li, X. 2021. A real-time detection algorithm for Kiwifruit defects based on YOLOv5. ELECTRONICS. 10(14):1711.
DOI: https://doi.org/10.3390/electronics10141711
Yu, C., Gao, C., Wang, J., Yu, G., Shen, C. and Sang, N. 2021. Bisenet v2: Bilateral network with guided aggregation for real-time semantic segmentation. Int. J. Comput. Vision. 129:3051-3068.
DOI: https://doi.org/10.1007/s11263-021-01515-2
Zhang, B., Huang, W., Gong, L., Li, J., Zhao, C., Liu, C., and Huang, D. 2015. Computer vision detection of defective apples using automatic lightness correction and weighted RVM classifier. J FOOD ENG. 146:143-151.
DOI: https://doi.org/10.1016/j.jfoodeng.2014.08.024
Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. 2017. Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2881-2890).
DOI: https://doi.org/10.1109/CVPR.2017.660
Zhou, H., Zhuang, Z., Liu, Y., Liu, Y., and Zhang, X. 2020. Defect classification of green plums based on deep learning. SENSORS, 20(23): 6993.
DOI: https://doi.org/10.3390/s20236993
How to Cite
Zhu, Y., Liu, S., Wu, X., Gao, L. and Xu, Y. (2024) “Multi-class segmentation of navel orange surface defects based on improved DeepLabv3+”, Journal of Agricultural Engineering. doi: 10.4081/jae.2024.1564.
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