摘要
近海船舶目标检测是一项非常具有挑战性的任务,受到学者专家广泛关注。基于卷积神经网络(CNN)和注意力机制的检测器在近海船舶目标检测方面的应用取得了显著成就。然而,船舶目标检测存在着表观相似和背景干扰导致检测过程中出现误检的问题。为此,本文提出了一种用于Faster RCNN (更快的基于区域的卷积神经网络)的表观细粒度辨别的检测头模块。该模块包括类别细粒度分支和高效全维动态卷积定位分支。其中类别细粒度分支通过全局特征建模和灵活的感知范围来挖掘和利用类别细粒度辨别特征,高效全维动态卷积定位分支通过高效灵活的感知船舶边界信息来区分目标与背景,从而减少误检漏检问题。通过在近海船舶公开数据集Seaships7000上进行实验验证,本文算法减少了误检漏检,提升了检测器性能。
Offshore ship object detection is a very challenging task and has received widespread attention from scholars and experts.Detectors based on Convolutional Neural Networks(CNN)and attention mechanisms have made significant progress in offshore ship object detection.However,the problem of false detection in the detection process is caused by the apparent similarity and background interference of ship targets.In order to solve this problem,this paper proposes a detection head module for fine-grained appearance discrimination implemented with Faster RCNN.This module includes a category fine-grained branch and an efficient full-dimensional dynamic convolution localization branch.The category fine-grained branch mines and utilizes category finegrained identification features through global feature modeling and flexible perception range.The efficient omni-dimensional dynamic convolution positioning branch distinguishes objects and backgrounds through the efficient and flexible perception of ship boundary information,thereby reducing false and missed detections.Through experimental verification on the offshore ship public dataset Seaships7000,the proposed algorithm reduces false detections and missed detections and improves detector performance.
作者
闵令通
范子满
窦飞阳
吕勤毅
李鑫
MIN Lingtong;FAN Ziman;DOU Feiyang;LYU Qinyi;LI Xin(School of Electronic Information,Northwestern Polytechnical University,Xi'an 710072,China)
出处
《遥测遥控》
2024年第2期1-9,共9页
Journal of Telemetry,Tracking and Command
基金
国家自然科学基金项目(62206221)
陕西省自然科学基础研究计划资助项目(2021JM-074)。
关键词
船舶目标检测
类别细粒度
表观判别
全维动态卷积
自注意力
Ship object detection
Similar feature extraction
Apparent discrimination
Dynamic convolution
Self-attention
