Liu, Q.; Huang, W.; Duan, X.; Wei, J.; Hu, T.; Yu, J.; Huang, J. DSW-YOLOv8n: A New Underwater Target Detection Algorithm Based on Improved YOLOv8n. Electronics2023, 12, 3892.
Liu, Q.; Huang, W.; Duan, X.; Wei, J.; Hu, T.; Yu, J.; Huang, J. DSW-YOLOv8n: A New Underwater Target Detection Algorithm Based on Improved YOLOv8n. Electronics 2023, 12, 3892.
Liu, Q.; Huang, W.; Duan, X.; Wei, J.; Hu, T.; Yu, J.; Huang, J. DSW-YOLOv8n: A New Underwater Target Detection Algorithm Based on Improved YOLOv8n. Electronics2023, 12, 3892.
Liu, Q.; Huang, W.; Duan, X.; Wei, J.; Hu, T.; Yu, J.; Huang, J. DSW-YOLOv8n: A New Underwater Target Detection Algorithm Based on Improved YOLOv8n. Electronics 2023, 12, 3892.
Abstract
Underwater target detection is widely used in various applications such as underwater search and rescue, underwater environment monitoring, and Marine resources survey. However, the visibility of the underwater environment and the accuracy of target detection can be affected by complex underwater light changes and unpredictable background noise. To address these issues, we propose an improved underwater target detection algorithm based on YOLOv8n. Our algorithm focuses on three aspects. Firstly, we replace the original C2f module with Deformable Convnets v2 to enhance the adaptive ability of the target region in the convolution check feature map and extract the target region's features more accurately. Secondly, we introduce SimAm, a non-parametric attention mechanism, which can deduce and assign three-dimensional attention weights without adding network parameters. Lastly, we optimize the loss function by replacing the CIOU loss function with the Wise-IOU loss function. To conduct our experiments, we create our own dataset of underwater target detection for experimentation. Meanwhile, we also utilized the Pascal VOC dataset to evaluate our approach. The mAP@0.5 and mAP@0.5:0.95 of the original YOLOv8n algorithm on the underwater target detection were 88.6% and 51.8%, respectively, and the improved algorithm mAP@0.5 and mAP@0.5:0.95 can reach 91.8% and 55.9%. The original YOLOV8n algorithm was 62.2% and 45.9% mAP@0.5 and mAP@0.5:0.95 on the Pascal VOC dataset, respectively. The improved YOLOV8n algorithm mAP@0.5 and mAP@0.5:0.95 were 65.7% and 48.3%, respectively. The floating-point computation volume of the model is reduced by about 6%. The above experimental results prove the effectiveness of our method.
Keywords
underwater target detection; deformable convnets v2; SimAm; Loss function
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
