基于ECA的YOLOv5水下鱼类目标检测

针对水下图像模糊、颜色失真,水下场景环境复杂、目标特征提取能力有限等导致的水下鱼类目标检测精确度低的问题,提出一种基于YOLOv5的改进水下鱼类目标检测算法.首先,针对水下图像模糊、颜色失真的问题,引入水下暗通道优先(underwater dark channel prior, UDCP)算法对图像进行预处理,有助于在不同环境下正确识别目标;然后,针对水下场景复杂、目标特征提取能力有限问题,在YOLOv5网络中引入高效的相关性通道(efficient channel attention, ECA),增强对目标的特征提取能力;最后,对损失函数进行改进,提高目标检测框的准确度.通过实验证明改进后的YOLOv5在水下鱼类目标检测中精确度比原始的YOLOv5提高了2.95%,平均检测精度(mAP@0.5:0.95)提高了5.52%.

ECA-based YOLOv5 Underwater Fish Target Detection

To address the low accuracy of underwater fish target detection caused by blurred and color-distorted underwater images, complex underwater scenes, and limited target feature extraction ability, this study proposes an improved underwater fish target detection algorithm based on YOLOv5. Firstly, in response to the blurring and color distortion of underwater images, the underwater dark channel prior (UDCP) algorithm is introduced to pre-process the images, which is helpful for correctly identifying the target in different environments. Then, considering the problems of complex underwater scenes and limited target feature extraction ability, the study introduces an efficient correlation channel, i.e., efficient channel attention (ECA), into the YOLOv5 network to enhance the feature extraction ability of the target. Finally, the loss function is improved to enhance the accuracy of the target detection box. Experiments show that the accuracy of the improved YOLOv5 in underwater fish target detection is 2.95% higher than that of the original YOLOv5, and the average detection accuracy (mAP@0.5:0.95) is increased by 5.52%.