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<p>Underwater imaging is frequently influenced by factors such as illumination, scattering, and refraction, which can result in low image contrast and blurriness. Moreover, the presence of numerous small, overlapping targets reduces detection accuracy. To address these challenges, first, green channel images are preprocessed to rectify color bias while improving contrast and clarity. Second, the YOLO-DBS network that employs deformable convolution is proposed to enhance feature learning from underwater blurry images. The ECA attention mechanism is also introduced to strengthen feature focus. Moreover, a bidirectional feature pyramid network is utilized for efficient multilayer feature fusion while removing nodes that contribute minimally to detection performance. In addition, the SIoU loss function that considers factors such as angular error and distance deviation is incorporated into the network. Validation on the RUOD dataset demonstrates that YOLO-DBS achieves approximately 3.1% improvement in <i>mAP</i>@0.5 compared with YOLOv8n and surpasses YOLOv9-tiny by 1.3%. YOLO-DBS reduces parameter count by 32% relative to YOLOv8n, thereby demonstrating superior performance in real-time detection on underwater observation platforms.</p>

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YOLO-DBS: Efficient Target Detection in Complex Underwater Scene Images Based on Improved YOLOv8

  • 期刊论文

  • 发表日期：

    2025年7月2日

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  • Xinhua Wang,
  • Xiangyang Song,
  • Zhuang Li,
  • Heqi Wang

摘要

Underwater imaging is frequently influenced by factors such as illumination, scattering, and refraction, which can result in low image contrast and blurriness. Moreover, the presence of numerous small, overlapping targets reduces detection accuracy. To address these challenges, first, green channel images are preprocessed to rectify color bias while improving contrast and clarity. Second, the YOLO-DBS network that employs deformable convolution is proposed to enhance feature learning from underwater blurry images. The ECA attention mechanism is also introduced to strengthen feature focus. Moreover, a bidirectional feature pyramid network is utilized for efficient multilayer feature fusion while removing nodes that contribute minimally to detection performance. In addition, the SIoU loss function that considers factors such as angular error and distance deviation is incorporated into the network. Validation on the RUOD dataset demonstrates that YOLO-DBS achieves approximately 3.1% improvement in mAP@0.5 compared with YOLOv8n and surpasses YOLOv9-tiny by 1.3%. YOLO-DBS reduces parameter count by 32% relative to YOLOv8n, thereby demonstrating superior performance in real-time detection on underwater observation platforms.