Target Detection Network for Underwater Image Based on Adaptive Anchor Frame and Re-parameterization. Issue 1 (1st November 2022)

• Record Type: Journal Article
• Authors: Xu, Huipu; Wang, Yiteng
• Is Part Of: Journal of physics. Volume 2363: Issue 1(2022)
• Publication Date: 2022-11-01
• Subjects: Target detection -- underwater image -- adaptive anchor frame -- re-parameterization; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2363/1/012012
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract : For low target detection rate and inaccurate localization of most target detection networks in special underwater environments, we propose an underwater target detection algorithm with adaptive anchor frames. The adaptive anchor frame selection strategy differs from the traditional methods of manually designing anchor frames and obtaining anchor frames using the K -means clustering algorithm. Our method is based on the K -means clustering algorithm and consists of a grouping strategy and a dynamic K -value strategy. It can also automatically calculate the appropriate numbers of anchor boxes and layers of network output based on the characteristics of label data. And performance of the network is guaranteed while avoiding the computation caused by redundant structures. The simple but powerful RepVGG classification network is used as the backbone network. Therefore, the feature extraction capability of the network is enhanced and the detection performance of the network on fuzzy targets is improved. The experimental results show that the algorithm in this paper achieves better detection results in the underwater target detection task compared to other algorithms.