Semantic Segmentation of Sika Deer Antler Image by U-Net Based on Two-Dimensional Discrete Wavelet Transform Fusion and Multi-Attention Mechanism
At present, the monitoring technology of the growth status of sika deer antlers faces many challenges in a complex breeding environment (such as light change, object occlusion, etc.). More importantly, an effective method for the segmentation of sika deer antlers is still lacking, which hinders the...
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MDPI AG
2025-05-01
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| author | Haotian Gong Jinfan Wei Yu Sun Zhipeng Li He Gong Juanjuan Fan |
| author_facet | Haotian Gong Jinfan Wei Yu Sun Zhipeng Li He Gong Juanjuan Fan |
| author_sort | Haotian Gong |
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| description | At present, the monitoring technology of the growth status of sika deer antlers faces many challenges in a complex breeding environment (such as light change, object occlusion, etc.). More importantly, an effective method for the segmentation of sika deer antlers is still lacking, which hinders the development of subsequent quality classification of sika deer antlers. In order to fill the research gap and lay a foundation for future sika deer antler quality classification, this paper proposed an improved semantic segmentation model based on U-Net, named SDAS-Net. In order to improve the segmentation accuracy and generalization ability of the model in a complex environment, we introduced a two-dimensional discrete wavelet transform module (2D-DWT) in the encoder head to reduce noise interference and enhance the ability to capture features. In order to compensate for the loss of feature information caused by 2D-DWT, we embedded the Star Blocks module in the encoder. In addition, the efficient mixed channel attention (EMCA) module was introduced to adaptively enhance key feature channels in the decoder, and the dual cross-attention mechanism (DCA) module was used to fuse high-dimensional features in skip connections. To verify the validity of the model, we constructed a 1055-image sika deer antler dataset (SDR). The experimental results show that compared with the baseline model, the performance of the SDAS-Net model is significantly improved, reaching 92.12% in MIoU and 93.63% in the PA index, and the number of parameters is only increased by 6.9%. The results show that the SDAS-Net model can effectively deal with the task of sika deer antler segmentation in a complex breeding environment while maintaining high precision. |
| format | Article |
| id | doaj-art-2bdbed6e22cf4266af26e93a8d54105a |
| institution | OA Journals |
| issn | 2076-2615 |
| language | English |
| publishDate | 2025-05-01 |
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| series | Animals |
| spelling | doaj-art-2bdbed6e22cf4266af26e93a8d54105a2025-08-20T01:56:57ZengMDPI AGAnimals2076-26152025-05-011510138810.3390/ani15101388Semantic Segmentation of Sika Deer Antler Image by U-Net Based on Two-Dimensional Discrete Wavelet Transform Fusion and Multi-Attention MechanismHaotian Gong0Jinfan Wei1Yu Sun2Zhipeng Li3He Gong4Juanjuan Fan5College of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Animal Science and Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaAt present, the monitoring technology of the growth status of sika deer antlers faces many challenges in a complex breeding environment (such as light change, object occlusion, etc.). More importantly, an effective method for the segmentation of sika deer antlers is still lacking, which hinders the development of subsequent quality classification of sika deer antlers. In order to fill the research gap and lay a foundation for future sika deer antler quality classification, this paper proposed an improved semantic segmentation model based on U-Net, named SDAS-Net. In order to improve the segmentation accuracy and generalization ability of the model in a complex environment, we introduced a two-dimensional discrete wavelet transform module (2D-DWT) in the encoder head to reduce noise interference and enhance the ability to capture features. In order to compensate for the loss of feature information caused by 2D-DWT, we embedded the Star Blocks module in the encoder. In addition, the efficient mixed channel attention (EMCA) module was introduced to adaptively enhance key feature channels in the decoder, and the dual cross-attention mechanism (DCA) module was used to fuse high-dimensional features in skip connections. To verify the validity of the model, we constructed a 1055-image sika deer antler dataset (SDR). The experimental results show that compared with the baseline model, the performance of the SDAS-Net model is significantly improved, reaching 92.12% in MIoU and 93.63% in the PA index, and the number of parameters is only increased by 6.9%. The results show that the SDAS-Net model can effectively deal with the task of sika deer antler segmentation in a complex breeding environment while maintaining high precision.https://www.mdpi.com/2076-2615/15/10/1388sika deer antlersemantic segmentationU-Net2D-DWTEMCA |
| spellingShingle | Haotian Gong Jinfan Wei Yu Sun Zhipeng Li He Gong Juanjuan Fan Semantic Segmentation of Sika Deer Antler Image by U-Net Based on Two-Dimensional Discrete Wavelet Transform Fusion and Multi-Attention Mechanism Animals sika deer antler semantic segmentation U-Net 2D-DWT EMCA |
| title | Semantic Segmentation of Sika Deer Antler Image by U-Net Based on Two-Dimensional Discrete Wavelet Transform Fusion and Multi-Attention Mechanism |
| title_full | Semantic Segmentation of Sika Deer Antler Image by U-Net Based on Two-Dimensional Discrete Wavelet Transform Fusion and Multi-Attention Mechanism |
| title_fullStr | Semantic Segmentation of Sika Deer Antler Image by U-Net Based on Two-Dimensional Discrete Wavelet Transform Fusion and Multi-Attention Mechanism |
| title_full_unstemmed | Semantic Segmentation of Sika Deer Antler Image by U-Net Based on Two-Dimensional Discrete Wavelet Transform Fusion and Multi-Attention Mechanism |
| title_short | Semantic Segmentation of Sika Deer Antler Image by U-Net Based on Two-Dimensional Discrete Wavelet Transform Fusion and Multi-Attention Mechanism |
| title_sort | semantic segmentation of sika deer antler image by u net based on two dimensional discrete wavelet transform fusion and multi attention mechanism |
| topic | sika deer antler semantic segmentation U-Net 2D-DWT EMCA |
| url | https://www.mdpi.com/2076-2615/15/10/1388 |
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