Abstract: In order to improve the detection accuracy of marine biological images, this paper uses optimized MSRCR to enhance marine biological images, and proposes an improved YOLOv4 algorithm（IYOLOv4） based on ASFF and Focal Loss. First of all, for light propagating in seawater, the strong attenuation of red light leads to the problem of low contrast and color shift in marine biological images. It uses bilateral filtering instead of Gaussian filtering in the traditional MSRCR, which not only preserves more image boundary features, but also solves the problem of image color shift by increasing the red in the image, at the same time the local contrast of the image is also improved. Secondly, the algorithm uses the ASFF structure to make full use of the semantic information of the high-level features of the image and the fine-grained features of the bottom layer, and fully integrates the features by learning the weight parameters to enhance the fusion effect. Finally, the BCE Loss used in the classification loss of YOLOv4 is replaced with Focal Loss to solve the problem of unbalanced categories in the dataset and improve detection accuracy. The experimental results show that compared with the YOLOv4 algorithm, the four classes of AP of holohurian, scallop, starfish, and echinus increases by 10.35, 9.13, 2.22, 0.14 percentage points respectively, and mAP increases by 5.45 percentage points.

Key words: image detection, YOLOv4, bilateral filter, adaptively spatial feature fusion（ASFF）, classification loss

摘要： 为提高海洋生物图像的检测精度，采用优化的MSRCR对海洋生物图像进行增强，并基于ASFF和Focal Loss提出一种改进的YOLOv4算法（IYOLOv4）。针对光线在海水中传播，红光的强衰减性，导致海洋生物图像对比度低、出现色偏的问题，使用双边滤波代替传统的MSRCR中的高斯滤波，不仅能保留更多图像边界的特征，而且通过增益图像中的红色，解决了图像色偏问题，同时也提高了图像局域对比度。算法使用ASFF结构充分利用图像高层特征的语义信息与底层的细粒度特征，通过学习权重参数的方式来进行特征的充分融合，增强融合效果；将YOLOv4的分类损失中采用的BCE Loss替换为Focal Loss，来解决数据集中类别不均衡的问题，提高检测精度。实验结果表明，该算法与YOLOv4算法相比，海参、扇贝、海星、海胆四种类别的AP分别提高了10.35、9.13、2.22、0.14个百分点，mAP提高了5.45个百分点。

关键词: 图像检测, YOLOv4, 双边滤波, 自适应空间特征融合（ASFF）, 分类损失