文献类型：期刊文献

英文题名：A Decoupled Head and Multiscale Coordinate Convolution Detection Method for Ship Targets in Optical Remote Sensing Images

作者：Ma, Jin[1];Fu, Dongyang[1];Wang, Difeng[2];Li, Yongze[1]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Guangdong, Peoples R China;[2]Minist Nat Resources, Inst Oceanog 2, State Key Lab Satellite Ocean Environm Dynam, Hangzhou 310012, Zhejiang, Peoples R China

年份：2024

卷号：12

起止页码：59831

外文期刊名：IEEE ACCESS

收录：SCI-EXPANDED(收录号：WOS:001214335700001)、、EI(收录号：20241816022622)、Scopus(收录号：2-s2.0-85191770970)、WOS

基金：No Statement Available

语种：英文

外文关键词：Target detection; coordinate embedding; multi-scale; decoupled head

外文摘要：Intelligent detection and recognition of ship targets is the basis of naval battlefield situational assessment and threat estimation. Its core task is to determine whether there is a ship target in the image and to detect, identify, and locate the ship target, which also has broad application prospects in fisheries management, maritime rescue, maritime traffic management, and marine environment monitoring. To this end, a multi-scale ship target detection improved algorithm for remote sensing images is proposed based on the YOLOv5 model, called CPE-YOLO. Firstly, a multi-scale attention fusion module based on coordinate position is proposed to solve the problem of missing spatial perception ability for ship detection in optical remote sensing images, to process the multi-scale coordinate information of feature maps, and to effectively establish long-range channel dependency among multi-scale channel attention. Secondly, a more refined feature pyramid network is constructed to effectively mitigate the impact of target scale variation due to remote sensing images on model performance, and to provide richer feature information for feature fusion layer regression localization. Ultimately, the decoupled head is integrated into the YOLO Head to disentangle the classifier and regressor, facilitating accelerated network convergence and concurrent enhancement of network detection accuracy. Finally, thorough tests on the HRSC2016, ASDC and SIGE datasets are presented to support our methodology. The results show that the performance of our proposed methods is better than other existing methods, with an mAP of 94.0%, 76.5%, and 98.7% on the HRSC2016, ASDC, and SIGE datasets. Moreover, comparative assessments against alternative deep learning methodologies affirm the sustained superiority of the enhanced method across overall performance metrics.