https://doi.org/10.4081/jae.2024.1564 Multi-class segmentation of navel orange surface defects based on improved DeepLabv3+
Published:20 February 2024
Abstract Views: 538
PDF: 159
HTML: 11
HTML: 11
Publisher's note
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.
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.
Authors
A multi-class segmentation model based on improved DeepLabv3+ is proposed to detect navel orange surface defects. This model aims to address the problems of the current mainstream semantic segmentation network, including rough edge segmentation of navel orange defects, poor accuracy of small target defect segmentation, and insufficient deep-level semantic extraction of defects, which will result in the loss of feature information. In order to improve semantic segmentation performance, the Coordinate Attention Mechanism is integrated into the DeepLabv3+ network. Additionally, the deformable empty convolution of the Atrous Spatial Pyramid Pooling structure replaces the dilated convolution, improving the network’s ability to fit and target irregular defects and shape changes. Furthermore, to achieve multi-scale feature fusion and enhance feature space and semantic information, a Bi-feature pyramid network-based feature fusion branch is added at the DeepLabv3+ encoder side. The experimental findings demonstrate that the improved DeepLabv3+ model improves the extraction capability of navel orange defect features and has better segmentation performance. On the navel orange surface defect dataset, the improved model’s average intersection ratio and average pixel intersection ratio accuracies are 77.32% and 86.38%, respectively, which are 3.81% and 5.29% higher than the original DeepLabv3+ network.
Bhargava, A., Bansal, A. Automatic detection and grading of multiple fruits by machine learning. 2020. Food Anal. Methods 13:751-61.
Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H. 2018. Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proceedings of the European conference on computer vision (ECCV). pp. 801-18.
Fan, S., Li, J., Zhang, Y., Tian, X., Wang, Q., He, X., Zhang, C., Huang, W. 2020. Online detection of defective apples using computer vision system combined with deep learning methods. J. Food Eng. 286:110102.
Hou, Q., Zhou, D., Feng, J. 2021. Coordinate attention for efficient mobile network design. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 13713-22.
Hu, J., Shen, L., Sun, G. 2018. Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7132-41.
Jin, L.I., Renyong, Z.H.A.O., Boxue, D.U., Liucheng, H., Kai, B. 2021. Research progress of nondestructive detection methods for defects of electrical epoxy insulators. Transact. Chin. Electrotechn. Soc. 36:4598-607.
Li J.B., Huang W.Q., Zhao C.J. 2015. Machine vision technology for detecting the external defects of fruits - A review. Image. Sci. J. 63:241-51.
Liang, X., Jia, X., Huang, W., He, X., Li, L., Fan, S., Li, J., Zhao, C., Zhang, C. 2022. Real-time grading of defect apples using semantic segmentation combination with a pruned YOLO V4 Network. Foods. 11:3150.
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S. 2017. Feature pyramid networks for object detection. Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2117-25.
Nithya, R., Santhi, B., Manikandan, R., Rahimi, M., Gandomi, A.H. 2022. Computer vision system for mango fruit defect detection using deep convolutional neural network. Foods. 11:3483.
Raman, S., Chougule, A., Chamola, V. 2022. A low power consumption mobile based IoT framework for real-time classification and segmentation for apple disease. Microprocess. Microsyst. 94:104656.
Ren, H.-E., Bai, J.-Y. 2013. Color grading based on dynamic clustering in lab color space. Jisuanji Gongcheng/Comput. Eng. 39.
Rong, D., Rao, X., 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.
Ronneberger, O., Fischer, P., Brox, T. 2015. U-net: convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. Springer International Publishing. pp. 234-41
Soltani Firouz, M., Sardari, H. 2022. Defect detection in fruit and vegetables by using machine vision systems and image processing. Food Eng. Rev. 14:353-79.
Strudel, R., Garcia, R., Laptev, I., Schmid, C. 2021. Segmenter: transformer for semantic segmentation. In Proceedings of the IEEE/CVF international conference on computer vision. pp. 7262-72.
Sun, X., Li, G., Xu, S. 2020. Fastidious attention network for navel orange segmentation. arXiv preprint arXiv:2003.11734.
Tian, K., Zeng, J., Song, T., Li, Z., Evans, A. Li, J. 2022. Tomato leaf diseases recognition based on deep convolutional neural networks. J. Agric. Eng. Res. 54.
Unay, D. 2022. Deep learning-based automatic grading of bi-colored apples using multispectral images. Multimed. Tools Appl. 81:38237-52.
Woo, S., Park, J., Lee, J.Y., Kweon, I.S. 2018. Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision. pp. 3-19.
Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M. Luo, P. 2021. SegFormer: simple and efficient design for semantic segmentation with transformers. Adv. Neur. In. 34:12077-90.
Xie, X., Ge, S., Xie, M., Hu, F., Jiang, N., Cai, T., Li, B. 2018. Image matching algorithm of defects on navel orange surface based on compressed sensing. J. Amb. Intel. Hum. Comp. pp. 1-9.
Yang, G.L., Luo, L., Feng, Y.Q., Zhao, H.S. 2014. Research of navel orange defect and color detection based on machine vision. Appl. Mech. Mater. 513:3442-5.
Yao, J., Qi, J., Zhang, J., Shao, H., Yang, J., Li, X. 2021. A realtime detection algorithm for Kiwifruit defects based on YOLOv5. Electronics. 10:1711.
Yu, C., Gao, C., Wang, J., Yu, G., Shen, C. Sang, N. 2021. Bisenet v2: Bilateral network with guided aggregation for real-time semantic segmentation. Int. J. Comput. Vision. 129:3051-68.
Zhang, B., Huang, W., Gong, L., Li, J., Zhao, C., Liu, C., Huang, D. 2015. Computer vision detection of defective apples using automatic lightness correction and weighted RVM classifier. J. Food Eng. 146:143-51.
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J. 2017. Pyramid scene parsing network. Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2881-90.
Zhou, H., Zhuang, Z., Liu, Y., Liu, Y., Zhang, X. 2020. Defect classification of green plums based on deep learning. Sensors. 20:6993.
How to Cite
Zhu, Y. (2024) “Multi-class segmentation of navel orange surface defects based on improved DeepLabv3+”, Journal of Agricultural Engineering, 55(2). doi: 10.4081/jae.2024.1564.
Copyright (c) 2024 the Author(s) 
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Maile Zhou, Jiajia Yang, Tingbo Xu, Jianjun Yin, Xinzhong Wang, Optimal design of transplanting mechanism with differential internal engagement non-circular gear trains , Journal of Agricultural Engineering: Vol. 53 No. 3 (2022)
- Xingbo Hu, Tian Xia, Leidong Yang, Fangming Wu, Ying Fan, Yinghong Tian, 3D modeling and volume measurement of bulk grains stored in large warehouses using bi-temporal multi-site terrestrial laser scanning data , Journal of Agricultural Engineering: Vol. 55 No. 1 (2024)
- Hemanthakumar R. Kappali, Sadyojatha K.M., Prashanthi S.K., Parametric evaluation of segmentation techniques for paddy diseases analysis , Journal of Agricultural Engineering: Vol. 54 No. 4 (2023)
- Zhongwei Hua, Min Guan, Lightweight sandy vegetation object detection algorithm based on attention mechanism , Journal of Agricultural Engineering: Vol. 54 No. 1 (2023)
- Xiong Bi, Hongchun Wang, Double-branch deep convolutional neural network-based rice leaf diseases recognition and classification , Journal of Agricultural Engineering: Vol. 55 No. 1 (2024)
- Bhola Paudel, Jayanta Kumar Basak, Seong Woo Jeon, Nibas Chandra Deb, Sijan Karki, Hyeon Tae Kim, Development and field testing of biodegradable seedling plug-tray cutting mechanism for automated vegetable transplanter , Journal of Agricultural Engineering: Vol. 55 No. 2 (2024)
- Fang Huimin, Niu Mengmeng, Zhu Zhengbo, Zhang Qingyi, Experimental and numerical investigations of the impacts of separating board and anti-blocking mechanism on maize seeding , Journal of Agricultural Engineering: Vol. 53 No. 1 (2022)
- Dhanashree Barbole, Parul M. Jadhav, Comparative analysis of 2D and 3D vineyard yield prediction system using artificial intelligence , Journal of Agricultural Engineering: Vol. 55 No. 1 (2024)
- Volodymyr Bulgakov, Valerii Adamchuk, Ladislav Nozdrovicky, Ivan Holovach, Theory of the interaction of flat sensing organ with the head of the sugar beet root , Journal of Agricultural Engineering: Vol. 48 No. 4 (2017)
- Aristide Timene, Haman Djalo, Design of a five-bar duckbill-type mechanism for sorghum transplanting , Journal of Agricultural Engineering: Vol. 54 No. 2 (2023)
You may also start an advanced similarity search for this article.
