文献类型：期刊文献

英文题名：Efficient detection method of deep-sea netting breakage based on attention and focusing on receptive-field spatial feature

作者：Yu, Guoyan[1,2];Su, Jinping[1,2];Luo, Yingtong[1,2];Chen, Zejia[1];Chen, Qibo[1];Chen, Shuaixing[1]

机构：[1]Guangdong Ocean Univ, Sch Mech Engn, Zhanjiang 524088, Guangdong, Peoples R China;[2]Guangdong Ocean Univ, Guangdong Marine Equipment & Mfg Engn Technol Res, Zhanjiang 524088, Guangdong, Peoples R China

年份：2024

卷号：18

期号：2

起止页码：1205

外文期刊名：SIGNAL IMAGE AND VIDEO PROCESSING

收录：SCI-EXPANDED(收录号：WOS:001091795900003)、、EI(收录号：20234415004523)、Scopus(收录号：2-s2.0-85175378378)、WOS

基金：This research was supported by the Guangdong Provincial Special Project for Marine Economic Development (Six Major Marine Industries) (GDNRC [2021] 42) and the Zhanjiang Key Laboratory of Modern Marine Fishery Equipment (2021A05023).

语种：英文

外文关键词：Deep-sea netting breakage; YOLOv7; Attention; Deep learning

外文摘要：Fish escapes due to breaches in deep-sea netting can affect local ecosystems. To accurately and quickly detect broken netting, we propose YOLOv7-net, an efficient deep-sea netting breakage detection method based on attention and focusing on the receptive-field spatial feature. First, Bi-level Routing Attention (BRA) is introduced to enhance the acquisition of feature information at different scales. Second, a new coordinated attention module (RFCAConv) that focuses on the spatial features of the receptive field is used to capture more detailed feature information. Finally, a new network module called CFE that integrates efficient channel attention (ECA) and FasterNet during cross-stage connections is designed, enhancing the ability of the network to express features while reducing the number of required parameters and computational complexity. The results obtained on a self-constructed broken netting dataset show that the precision, recall, AP, F1 score and detection speed of YOLOv7-net are 2.8%, 1.8%, 2.4%, 2%, and 8.92 fps higher than those of YOLOv7, respectively, and the proposed approach can be specifically used to identify deep-sea netting damage. Our method improves the efficiency of broken netting detection in complex marine environments, providing new insights into the development of mariculture equipment and the protection of ecosystems.