A Real-Time Semantic Segmentation Method of Sheep Carcass Images Based on ICNet
How to realize the accurate recognition of 3 parts of sheep carcass is the key to the research of mutton cutting robots. The characteristics of each part of the sheep carcass are connected to each other and have similar features, which make it difficult to identify and detect, but with the developme...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2021/8847984 |
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| _version_ | 1832547019239981056 |
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| author | Shida Zhao Guangzhao Hao Yichi Zhang Shucai Wang |
| author_facet | Shida Zhao Guangzhao Hao Yichi Zhang Shucai Wang |
| author_sort | Shida Zhao |
| collection | DOAJ |
| description | How to realize the accurate recognition of 3 parts of sheep carcass is the key to the research of mutton cutting robots. The characteristics of each part of the sheep carcass are connected to each other and have similar features, which make it difficult to identify and detect, but with the development of image semantic segmentation technology based on deep learning, it is possible to explore this technology for real-time recognition of the 3 parts of the sheep carcass. Based on the ICNet, we propose a real-time semantic segmentation method for sheep carcass images. We first acquire images of the sheep carcass and use augmentation technology to expand the image data, after normalization, using LabelMe to annotate the image and build the sheep carcass image dataset. After that, we establish the ICNet model and train it with transfer learning. The segmentation accuracy, MIoU, and the average processing time of single image are then obtained and used as the evaluation standard of the segmentation effect. In addition, we verify the generalization ability of the ICNet for the sheep carcass image dataset by setting different brightness image segmentation experiments. Finally, the U-Net, DeepLabv3, PSPNet, and Fast-SCNN are introduced for comparative experiments to further verify the segmentation performance of the ICNet. The experimental results show that for the sheep carcass image datasets, the segmentation accuracy and MIoU of our method are 97.68% and 88.47%, respectively. The single image processing time is 83 ms. Besides, the MIoU of U-Net and DeepLabv3 is 0.22% and 0.03% higher than the ICNet, but the processing time of a single image is longer by 186 ms and 430 ms. Besides, compared with the PSPNet and Fast-SCNN, the MIoU of the ICNet model is increased by 1.25% and 4.49%, respectively. However, the processing time of a single image is shorter by 469 ms and expands by 7 ms, respectively. |
| format | Article |
| id | doaj-art-799853987acb4596968a0ef1bfa21a02 |
| institution | Kabale University |
| issn | 1687-9600 1687-9619 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Robotics |
| spelling | doaj-art-799853987acb4596968a0ef1bfa21a022025-02-03T06:46:16ZengWileyJournal of Robotics1687-96001687-96192021-01-01202110.1155/2021/88479848847984A Real-Time Semantic Segmentation Method of Sheep Carcass Images Based on ICNetShida Zhao0Guangzhao Hao1Yichi Zhang2Shucai Wang3Departments of Engineering, University of Huazhong Agricultural, Wuhan, Hubei 430070, ChinaDepartments of Engineering, University of Huazhong Agricultural, Wuhan, Hubei 430070, ChinaDepartments of Engineering, University of Huazhong Agricultural, Wuhan, Hubei 430070, ChinaDepartments of Engineering, University of Huazhong Agricultural, Wuhan, Hubei 430070, ChinaHow to realize the accurate recognition of 3 parts of sheep carcass is the key to the research of mutton cutting robots. The characteristics of each part of the sheep carcass are connected to each other and have similar features, which make it difficult to identify and detect, but with the development of image semantic segmentation technology based on deep learning, it is possible to explore this technology for real-time recognition of the 3 parts of the sheep carcass. Based on the ICNet, we propose a real-time semantic segmentation method for sheep carcass images. We first acquire images of the sheep carcass and use augmentation technology to expand the image data, after normalization, using LabelMe to annotate the image and build the sheep carcass image dataset. After that, we establish the ICNet model and train it with transfer learning. The segmentation accuracy, MIoU, and the average processing time of single image are then obtained and used as the evaluation standard of the segmentation effect. In addition, we verify the generalization ability of the ICNet for the sheep carcass image dataset by setting different brightness image segmentation experiments. Finally, the U-Net, DeepLabv3, PSPNet, and Fast-SCNN are introduced for comparative experiments to further verify the segmentation performance of the ICNet. The experimental results show that for the sheep carcass image datasets, the segmentation accuracy and MIoU of our method are 97.68% and 88.47%, respectively. The single image processing time is 83 ms. Besides, the MIoU of U-Net and DeepLabv3 is 0.22% and 0.03% higher than the ICNet, but the processing time of a single image is longer by 186 ms and 430 ms. Besides, compared with the PSPNet and Fast-SCNN, the MIoU of the ICNet model is increased by 1.25% and 4.49%, respectively. However, the processing time of a single image is shorter by 469 ms and expands by 7 ms, respectively.http://dx.doi.org/10.1155/2021/8847984 |
| spellingShingle | Shida Zhao Guangzhao Hao Yichi Zhang Shucai Wang A Real-Time Semantic Segmentation Method of Sheep Carcass Images Based on ICNet Journal of Robotics |
| title | A Real-Time Semantic Segmentation Method of Sheep Carcass Images Based on ICNet |
| title_full | A Real-Time Semantic Segmentation Method of Sheep Carcass Images Based on ICNet |
| title_fullStr | A Real-Time Semantic Segmentation Method of Sheep Carcass Images Based on ICNet |
| title_full_unstemmed | A Real-Time Semantic Segmentation Method of Sheep Carcass Images Based on ICNet |
| title_short | A Real-Time Semantic Segmentation Method of Sheep Carcass Images Based on ICNet |
| title_sort | real time semantic segmentation method of sheep carcass images based on icnet |
| url | http://dx.doi.org/10.1155/2021/8847984 |
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