CollabOD: Collaborative Multi-Backbone with Cross-scale Vision for UAV Small Object Detection
This work tackles the problem of improving small object detection in UAV imagery for applications requiring robust and efficient perception, offering an incremental improvement to existing methods.
This paper addresses the challenge of small object detection in UAV imagery, which suffers from scale variation and detail degradation. The authors propose CollabOD, a lightweight collaborative detection framework that preserves structural details and aligns heterogeneous feature streams, leading to enhanced representation stability and efficient inference.
Small object detection in unmanned aerial vehicle (UAV) imagery is challenging, mainly due to scale variation, structural detail degradation, and limited computational resources. In high-altitude scenarios, fine-grained features are further weakened during hierarchical downsampling and cross-scale fusion, resulting in unstable localization and reduced robustness. To address this issue, we propose CollabOD, a lightweight collaborative detection framework that explicitly preserves structural details and aligns heterogeneous feature streams before multi-scale fusion. The framework integrates Structural Detail Preservation, Cross-Path Feature Alignment, and Localization-Aware Lightweight Design strategies. From the perspectives of image processing, channel structure, and lightweight design, it optimizes the architecture of conventional UAV perception models. The proposed design enhances representation stability while maintaining efficient inference. A unified detail-aware detection head further improves regression robustness without introducing additional deployment overhead. The code is available at: https://github.com/Bai-Xuecheng/CollabOD.