Abstract:

Due to the unique characteristics of underwater creatures， underwater images usually exit many small targets being hard to detect and often overlapping with each other. In addition， light absorption and scattering in underwater environment can cause underwater images' color offset and blur. To overcome those challenges， an underwater target detection algorithm， namely WCA-YOLOv8， was proposed. Firstly， the Feature Fusion Module （FFM） was designed to improve the focus on spatial dimension in order to improve the recognition ability for targets with color offset and blur. Secondly， the FReLU Coordinate Attention （FCA） module was added to enhance the feature extraction ability for overlapped and occluded underwater targets. Thirdly， Complete Intersection over Union （CIoU） loss function was replaced by Wise-IoU version 3 （WIoU v3） loss function to strengthen the detection performance for small size targets. Finally， the Downsampling Enhancement Module （DEM） was designed to preserve context information during feature extraction more completely. Experimental results show that WCA-YOLOv8 achieves 75.8% and 88.6% mean Average Precision （mAP_0.5） and 60 frame/s and 57 frame/s detection speeds on RUOD and URPC datasets， respectively. Compared with other state-of-the-art underwater target detection algorithms， WCA-YOLOv8 can achieve higher detection accuracy with faster detection speed.

Key words: YOLOv8, underwater target detection, feature fusion, Wise-IoU version 3 (WIoU v3) loss function

摘要：

由于水下生物的特性，水下图像中存在较多难以检测的小目标，且目标之间经常相互遮挡，而水下环境中的光线吸收和散射也会造成水下图像的颜色偏移和模糊。针对上述问题，提出水下目标检测算法WCA-YOLOv8。首先，设计特征融合模块（FFM），增强对空间维度信息的关注，提升对模糊和颜色偏移目标的识别能力；其次，加入FCA（FReLU Coordinate Attention）模块，增强对相互重叠、遮挡水下目标的特征提取能力；再次，为了提高模型对水下小目标的检测性能，将完整交并比（CIoU）损失函数替换为WIoU v3（Wise-IoU version 3）损失函数；最后，设计下采样增强模块（DEM），使特征提取过程中保存的上下文信息更完整，改善水下目标检测的性能。RUOD和URPC数据集上的实验结果表明，WCA-YOLOv8的检测平均精度均值（mAP_0.5）分别为75.8%和88.6%，检测速度分别为60 frame/s和57 frame/s。与其他前沿的水下物体检测算法相比，WCA-YOLOv8不仅能够获得更高的检测准确性，还可达到更快的检测速度。

关键词: YOLOv8, 水下目标检测, 特征融合, WIoU v3损失函数