文档介绍：: .
计算机工程与衡的问题，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 %、%、%、%，mAP 提高了 %。
关键词：图像检测；YOLOv4；双边滤波；ASFF；分类损失
文献标志码：A 中图分类号： doi：.1002--0556
An Image Enhancement and Improved Marine Biological Image Detection Algorithm
GUO Pingxiu, LI Qinan, YANG Zhongpeng
School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
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. The use of bilateral filtering instead of Gaussian filtering in the traditional
MSRCR, which not only preserves more image boundary features, but also the problem of image color shift is solved 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 experiment results show that
compared with the YOLOv4 algorithm, the four classes of AP of holohurian, scallop, starfish, and echinus incr