Underwater Target Detection Based on Improved YOLOv7
Underwater target detection is a crucial aspect of ocean exploration. However, conventional underwater target detection methods face several challenges such as inaccurate feature extraction, slow detection speed, and lack of robustness in complex underwater environments. To address these limitations...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/11/3/677 |
| _version_ | 1797610920131690496 |
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| author | Kaiyue Liu Qi Sun Daming Sun Lin Peng Mengduo Yang Nizhuan Wang |
| author_facet | Kaiyue Liu Qi Sun Daming Sun Lin Peng Mengduo Yang Nizhuan Wang |
| author_sort | Kaiyue Liu |
| collection | DOAJ |
| description | Underwater target detection is a crucial aspect of ocean exploration. However, conventional underwater target detection methods face several challenges such as inaccurate feature extraction, slow detection speed, and lack of robustness in complex underwater environments. To address these limitations, this study proposes an improved YOLOv7 network (YOLOv7-AC) for underwater target detection. The proposed network utilizes an ACmixBlock module to replace the 3 × 3 convolution block in the E-ELAN structure, and incorporates jump connections and 1 × 1 convolution architecture between ACmixBlock modules to improve feature extraction and network reasoning speed. Additionally, a ResNet-ACmix module is designed to avoid feature information loss and reduce computation, while a Global Attention Mechanism (GAM) is inserted in the backbone and head parts of the model to improve feature extraction. Furthermore, the K-means++ algorithm is used instead of K-means to obtain anchor boxes and enhance model accuracy. Experimental results show that the improved YOLOv7 network outperforms the original YOLOv7 model and other popular underwater target detection methods. The proposed network achieved a mean average precision (mAP) value of 89.6% and 97.4% on the URPC dataset and Brackish dataset, respectively, and demonstrated a higher frame per second (FPS) compared to the original YOLOv7 model. In conclusion, the improved YOLOv7 network proposed in this study represents a promising solution for underwater target detection and holds great potential for practical applications in various underwater tasks. |
| first_indexed | 2024-03-11T06:20:42Z |
| format | Article |
| id | doaj.art-fa1d72ae20db470780534984fb785421 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-11T06:20:42Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-fa1d72ae20db470780534984fb7854212023-11-17T11:58:56ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-03-0111367710.3390/jmse11030677Underwater Target Detection Based on Improved YOLOv7Kaiyue Liu0Qi Sun1Daming Sun2Lin Peng3Mengduo Yang4Nizhuan Wang5Jiangsu Key Laboratory of Marine Bioresources and Environment/Jiangsu Key Laboratory of Marine Biotechnology/Co-Innovation Center of Jiangsu Marine Bio-Industry Technology, Jiangsu Ocean University, Lianyungang 222005, ChinaBeijing KnowYou Technology Co., Ltd., Beijing 100086, ChinaBeijing KnowYou Technology Co., Ltd., Beijing 100086, ChinaJiangsu Key Laboratory of Marine Bioresources and Environment/Jiangsu Key Laboratory of Marine Biotechnology/Co-Innovation Center of Jiangsu Marine Bio-Industry Technology, Jiangsu Ocean University, Lianyungang 222005, ChinaProvincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou 215301, ChinaJiangsu Key Laboratory of Marine Bioresources and Environment/Jiangsu Key Laboratory of Marine Biotechnology/Co-Innovation Center of Jiangsu Marine Bio-Industry Technology, Jiangsu Ocean University, Lianyungang 222005, ChinaUnderwater target detection is a crucial aspect of ocean exploration. However, conventional underwater target detection methods face several challenges such as inaccurate feature extraction, slow detection speed, and lack of robustness in complex underwater environments. To address these limitations, this study proposes an improved YOLOv7 network (YOLOv7-AC) for underwater target detection. The proposed network utilizes an ACmixBlock module to replace the 3 × 3 convolution block in the E-ELAN structure, and incorporates jump connections and 1 × 1 convolution architecture between ACmixBlock modules to improve feature extraction and network reasoning speed. Additionally, a ResNet-ACmix module is designed to avoid feature information loss and reduce computation, while a Global Attention Mechanism (GAM) is inserted in the backbone and head parts of the model to improve feature extraction. Furthermore, the K-means++ algorithm is used instead of K-means to obtain anchor boxes and enhance model accuracy. Experimental results show that the improved YOLOv7 network outperforms the original YOLOv7 model and other popular underwater target detection methods. The proposed network achieved a mean average precision (mAP) value of 89.6% and 97.4% on the URPC dataset and Brackish dataset, respectively, and demonstrated a higher frame per second (FPS) compared to the original YOLOv7 model. In conclusion, the improved YOLOv7 network proposed in this study represents a promising solution for underwater target detection and holds great potential for practical applications in various underwater tasks.https://www.mdpi.com/2077-1312/11/3/677underwater target detectionmarine resourcescomputer visionimage analysisYOLOv7-ACGAM |
| spellingShingle | Kaiyue Liu Qi Sun Daming Sun Lin Peng Mengduo Yang Nizhuan Wang Underwater Target Detection Based on Improved YOLOv7 Journal of Marine Science and Engineering underwater target detection marine resources computer vision image analysis YOLOv7-AC GAM |
| title | Underwater Target Detection Based on Improved YOLOv7 |
| title_full | Underwater Target Detection Based on Improved YOLOv7 |
| title_fullStr | Underwater Target Detection Based on Improved YOLOv7 |
| title_full_unstemmed | Underwater Target Detection Based on Improved YOLOv7 |
| title_short | Underwater Target Detection Based on Improved YOLOv7 |
| title_sort | underwater target detection based on improved yolov7 |
| topic | underwater target detection marine resources computer vision image analysis YOLOv7-AC GAM |
| url | https://www.mdpi.com/2077-1312/11/3/677 |
| work_keys_str_mv | AT kaiyueliu underwatertargetdetectionbasedonimprovedyolov7 AT qisun underwatertargetdetectionbasedonimprovedyolov7 AT damingsun underwatertargetdetectionbasedonimprovedyolov7 AT linpeng underwatertargetdetectionbasedonimprovedyolov7 AT mengduoyang underwatertargetdetectionbasedonimprovedyolov7 AT nizhuanwang underwatertargetdetectionbasedonimprovedyolov7 |