LCB-Net: Long-Range Context and Box Distribution Network for Small Object Detection

Small object detection (SOD) remains a critical challenge in computer vision, with vital applications in areas like UAV inspection, autonomous driving, and medical image analysis. Existing methods are often limited by inadequate feature representation for small objects and insufficient utilization of contextual information. To tackle these issues, this paper proposes a novel LCB-Net. First, we design a plug-and-play Saliency-guided Long-range Mamba (SL-Mamba) module, which leverages spatially attentive maps from shallow features to explicitly guide the model’s focus toward small target regions. This module captures long-range contextual dependencies through state space modeling and enhances local–global feature synergy via cross-scale fusion. Second, we introduce a Bounding Box Distribution Loss (BDL) that employs label distribution learning (LDL) to explicitly model localization ambiguity and improve accuracy. Extensive experiments on standard small object benchmarks such as VisDrone, WiderPerson, and NWPU-VHR-10 demonstrate that our approach achieves significant performance gains over strong baselines. Specifically, on the VisDrone dataset, it yields a 4.3% improvement in mAP@0.5:0.95. Furthermore, evaluations across small object benchmarks and the general-purpose MS-COCO dataset confirm that the proposed BDL consistently surpasses traditional IoU-based losses, including CIoU and ProbIoU, in localization tasks.