文献类型：期刊文献

英文题名：Target-Focused Enhancement Network for Distant Infrared Dim and Small Target Detection

作者：Tong, Yunfei[1,2];Leng, Yue[1,2];Yang, Hai[2];Wang, Zhe[1,2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China

年份：2024

卷号：62

外文期刊名：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

收录：;EI(收录号：20244017143470);WOS:【SCI-EXPANDED(收录号:WOS:001327427200010)】；

基金：This work was supported in part by the Natural Science Foundation of China under Grant 62476087 and in part by the Chinese Defense Program of Science and Technology under Grant 2021-JCJQ-JJ-0041.

语种：英文

外文关键词：Feature extraction; Object detection; Training; Semantics; Transformers; Data models; Data augmentation; Interference; Attention mechanisms; Internet; Adaptive fusion; autoaugment; long-distance constraint (LDC); long-distance infrared small targets

摘要：In the context of long-range infrared detection of small targets, complex battlefield environments with strong background effects, adverse weather conditions, and intense light interference pose significant challenges. These factors contribute to a low signal-to-noise ratio and limited target information in infrared imagery. To address these challenges, a feature enhancement network called target-focused enhancement network (TENet) is proposed with two key innovations: the dense long-distance constraint (DLDC) module and the autoaugmented copy-paste bounding-box (ACB) strategy. The DLDC module incorporates a self-attention mechanism to provide the model with a global understanding of the relationship between small targets and the backgrounds. By integrating a multiscale structure and using dense connections, this module effectively transfers global information to the deep layers, thus enhancing the semantic features. On the other hand, the ACB strategy focuses on data enhancement, particularly increasing the target representation. This approach addresses the challenge of distributional bias between small targets and background using context information for target segmentation, mapping augment strategies to 2-D space, and using an adaptive paste method to fuse the target with the background. The DLDC module and the ACB strategy complement each other in terms of features and data, leading to a significant improvement in model performance. Experiments on infrared datasets with complex backgrounds demonstrate that the proposed network achieves superior performance in detecting dim and small targets.