Underwater target detection algorithm based on improved YOLOv4 with SemiDSConv and FIoU loss function

Abstract

Underwater target detection is an indispensable part of marine environmental engineering and a fast and accurate method of detecting underwater targets is essential. Although many target detection algorithms have achieved great accuracy in daily scenes, there are issues of low-quality images due to the complex underwater environment, which makes applying these deep learning algorithms directly to process underwater target detection tasks difficult. In this paper, we presented an algorithm for underwater target detection based on improved You Only Look Once (YOLO) v4 in response to the underwater environment. First, we developed a new convolution module and network structure. Second, a new intersection over union loss was defined to substitute the original loss function. Finally, we integrated some other useful strategies to achieve more improvement, such as adding one more prediction head to detect targets of varying sizes, integrating the channel attention into the network, utilizing K-means++ to cluster anchor box, and utilizing different activation functions. The experimental results indicate that, in comparison with YOLOv4, our proposed algorithm improved the average accuracy of the underwater dataset detection by 10.9%, achieving 91.1%, with a detection speed of 58.1 frames per second. Therefore, compared to other mainstream target detection algorithms, it is superior and feasible for applications in intricate underwater environments.