A lightweight underwater fish image semantic segmentation model based on U‐Net
Abstract Semantic segmentation of underwater fish images is vital for monitoring fish stocks, assessing marine resources, and sustaining fisheries. To tackle challenges such as low segmentation accuracy, inadequate real‐time performance, and imprecise location segmentation in current methods, a nove...
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| Format: | Article |
| Language: | English |
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Wiley
2024-10-01
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| Series: | IET Image Processing |
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| Online Access: | https://doi.org/10.1049/ipr2.13161 |
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| author | Zhenkai Zhang Wanghua Li Boon‐Chong Seet |
| author_facet | Zhenkai Zhang Wanghua Li Boon‐Chong Seet |
| author_sort | Zhenkai Zhang |
| collection | DOAJ |
| description | Abstract Semantic segmentation of underwater fish images is vital for monitoring fish stocks, assessing marine resources, and sustaining fisheries. To tackle challenges such as low segmentation accuracy, inadequate real‐time performance, and imprecise location segmentation in current methods, a novel lightweight U‐Net model is proposed. The proposed model acquires more segmentation details by applying a multiple‐input approach at the first four encoder levels. To achieve both lightweight and high accuracy, a multi‐scale residual structure (MRS) module is proposed to reduce parameters and compensate for the accuracy loss caused by the reduction of channels. To improve segmentation accuracy, a multi‐scale skip connection (MSC) structure is further proposed, and the convolution block attention mechanism (CBAM) is introduced at the end of each decoder level for weight adjustment. Experimental results demonstrate a notable reduction in model volume, parameters, and floating‐point operations by 94.20%, 94.39%, and 51.52% respectively, compared to the original model. The proposed model achieves a high mean intersection over union (mIOU) of 94.44%, mean pixel accuracy (mPA) of 97.03%, and a frame rate of 43.62 frames per second (FPS). With its high precision and minimal parameters, the model strikes a balance between accuracy and speed, making it particularly suitable for underwater image segmentation. |
| format | Article |
| id | doaj-art-68190b95e42e4d368e1e963642bb0eda |
| institution | OA Journals |
| issn | 1751-9659 1751-9667 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj-art-68190b95e42e4d368e1e963642bb0eda2025-08-20T02:12:20ZengWileyIET Image Processing1751-96591751-96672024-10-0118123143315510.1049/ipr2.13161A lightweight underwater fish image semantic segmentation model based on U‐NetZhenkai Zhang0Wanghua Li1Boon‐Chong Seet2Ocean College Jiangsu University of Science and Technology Zhenjiang People's Republic of ChinaOcean College Jiangsu University of Science and Technology Zhenjiang People's Republic of ChinaDepartment of Electrical and Electronic Engineering Auckland University of Technology Auckland New ZealandAbstract Semantic segmentation of underwater fish images is vital for monitoring fish stocks, assessing marine resources, and sustaining fisheries. To tackle challenges such as low segmentation accuracy, inadequate real‐time performance, and imprecise location segmentation in current methods, a novel lightweight U‐Net model is proposed. The proposed model acquires more segmentation details by applying a multiple‐input approach at the first four encoder levels. To achieve both lightweight and high accuracy, a multi‐scale residual structure (MRS) module is proposed to reduce parameters and compensate for the accuracy loss caused by the reduction of channels. To improve segmentation accuracy, a multi‐scale skip connection (MSC) structure is further proposed, and the convolution block attention mechanism (CBAM) is introduced at the end of each decoder level for weight adjustment. Experimental results demonstrate a notable reduction in model volume, parameters, and floating‐point operations by 94.20%, 94.39%, and 51.52% respectively, compared to the original model. The proposed model achieves a high mean intersection over union (mIOU) of 94.44%, mean pixel accuracy (mPA) of 97.03%, and a frame rate of 43.62 frames per second (FPS). With its high precision and minimal parameters, the model strikes a balance between accuracy and speed, making it particularly suitable for underwater image segmentation.https://doi.org/10.1049/ipr2.13161computer visionconvolutional neural netsimage segmentationoceanographic techniques |
| spellingShingle | Zhenkai Zhang Wanghua Li Boon‐Chong Seet A lightweight underwater fish image semantic segmentation model based on U‐Net IET Image Processing computer vision convolutional neural nets image segmentation oceanographic techniques |
| title | A lightweight underwater fish image semantic segmentation model based on U‐Net |
| title_full | A lightweight underwater fish image semantic segmentation model based on U‐Net |
| title_fullStr | A lightweight underwater fish image semantic segmentation model based on U‐Net |
| title_full_unstemmed | A lightweight underwater fish image semantic segmentation model based on U‐Net |
| title_short | A lightweight underwater fish image semantic segmentation model based on U‐Net |
| title_sort | lightweight underwater fish image semantic segmentation model based on u net |
| topic | computer vision convolutional neural nets image segmentation oceanographic techniques |
| url | https://doi.org/10.1049/ipr2.13161 |
| work_keys_str_mv | AT zhenkaizhang alightweightunderwaterfishimagesemanticsegmentationmodelbasedonunet AT wanghuali alightweightunderwaterfishimagesemanticsegmentationmodelbasedonunet AT boonchongseet alightweightunderwaterfishimagesemanticsegmentationmodelbasedonunet AT zhenkaizhang lightweightunderwaterfishimagesemanticsegmentationmodelbasedonunet AT wanghuali lightweightunderwaterfishimagesemanticsegmentationmodelbasedonunet AT boonchongseet lightweightunderwaterfishimagesemanticsegmentationmodelbasedonunet |