计算机工程与应用2024，Vol.60Issue(11)：224-232,9.DOI:10.3778/j.issn.1002-8331.2302-0051
基于特征交互结构的弱光目标检测
Low-Light Object Detection Based on Feature Interaction Structure
摘要
Abstract
Aiming at the problem that the current mainstream and advanced object detection algorithms have low detec-tion accuracy in low-light scenes,it is analyzed that the low-light image weakens the local correlation induction bias that the traditional convolutional neural network relies on,and introduces the Swin Transformer stage with excellent modeling ability for global features to achieve global attention and enhance the amount of feature information.The global attention is combined with local convolution to extract the features of low-light image in parallel,and a feature interaction structure(FIS)is proposed.Through the carefully designed secondary interaction mode,local and global information can be effectively analyzed,utilized and combined.The interactive parallel dual-stream backbone network FISNet is constructed based on the FIS stack,which realizes the deep fusion of the two types of features,and provides a hierarchical feature structure that is very important for intensive predictive tasks.FISNet has achieved 40.6 AP on the low-light image data set ExDark,and has achieved+0.5～2.9 AP detection accuracy improvement compared with the benchmark model such as EfficientNet,which has good application in low-light object detection scenarios.关键词
弱光图像/目标检测/全局特征/特征交互结构Key words
low-light images/object detection/global features/feature interaction structure分类
信息技术与安全科学引用本文复制引用
麦锦文,李浩,康雁..基于特征交互结构的弱光目标检测[J].计算机工程与应用,2024,60(11):224-232,9.基金项目
国家自然科学基金(61762092) （61762092）
云南省重大科技专项(202002AD080047,202202AE090019,202202AD080007). （202002AD080047,202202AE090019,202202AD080007）
