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| Underwater Target Detection Algorithm Based on Improved FCOS |
| CHEN Wei-dong1,2,XIE Xiao-dong1,CEN Qiang1,CHEN Na-lan1,ZHU Qi-guang1,2 |
1. School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
2. Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China |
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Abstract Propose an underwater target detection algorithm based on an improved fully convolutional one-stage object detection (FCOS). In response to problems such as high color cast, low contrast, dark color bias, and blurry distortion in underwater optical images that result in poor performance of existing target detection algorithms in underwater environments, the ordinary convolutions in the backbone network are replaced with deformable convolutions (DCN) for optimization, enhancing the algorithm's feature extraction capability in blurry underwater images. The feature fusion network and detection network of the improved network are enhanced through neural architecture search (NAS) to improve the utilization of features extracted by the backbone network. The CIoU Loss is adopted as the new loss function to improve the accuracy of coordinate regression. Experimental results show that the algorithm improves the detection accuracy by 1.8% and recall rate by 2.2% on the DUO dataset, with a detection speed of 53.4 frames /s, which is a 5.0% reduction compared to the previous version. The algorithm achieves high accuracy and meets the requirements of real-time detection.
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Received: 16 February 2023
Published: 17 November 2023
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| Fund:National Natural Science Foundation |
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2023, Vol. 44 
