西安电子科技大学学报（自然科学版）2024，Vol.51Issue(4)：1-14,14.DOI:10.19665/j.issn1001-2400.20231203
平滑交互式压缩网络的红外小目标检测算法
Smooth interactive compression network for infrared small target detection
摘要
Abstract
Infrared Small Target Detection is a critical focus of various fields,including earth observation and disaster relief efforts,receiving considerable attention within the academic community.Since infrared small targets often occupy just a few dozen pixels and are scattered within complex backgrounds,it becomes paramount to extract semantic information from a broad range of image features to distinguish targets from their surroundings and enhance detection performance.Traditional convolutional neural networks,due to their limited receptive fields and substantial computational demands,face challenges in effectively capturing the shape and precise positioning of small targets,leading to missed detections and false alarms.In response to these challenges,this paper proposes a novel Smooth Interactive Compression Network comprising two main components:the Smooth Interaction Module and the Cross Compression Module.The Smooth Interaction Module extends the feature map′s receptive field and enhances inter-feature dependencies,thus bolstering the network's detection robustness in complex background scenarios.The Cross Compression Module takes into account channel contributions and the interpretability of pruning,dynamically fusing feature maps of varying resolutions.Extensive experiments conducted on the publicly available SIRST dataset and IRSTD-1 K dataset demonstrate that the proposed network effectively addresses issues such as target loss,a high false alarm rate,and subpar visual results.Taking the SIRST dataset as an example,compared to the second-best performing model,the proposed model achieved a remarkable improvement in metrics:IoU,nIoU,and Pd are increased by 3.05%,3.41%,and 1.02%,respectively.Meanwhile,Fa and FLOPs are decreased by 33.33% and 82.30%,respectively.关键词
红外小目标检测/深度学习/网络编码/模型压缩Key words
Infrared Small Target Detection/deep learning/network coding/model compression分类
信息技术与安全科学引用本文复制引用
张铭津,周楠,李云松..平滑交互式压缩网络的红外小目标检测算法[J].西安电子科技大学学报（自然科学版）,2024,51(4):1-14,14.基金项目
国家自然科学基金(62272363,62036007) （62272363,62036007）
中国科协青年人才托举工程(2021QNRC001) （2021QNRC001）
星载计算机与电子技术创新联合实验室2023年度开放基金(2024KFKT001-1) （2024KFKT001-1）
