CollabOD: Collaborative Multi-Backbone with Cross-scale Vision for UAV Small Object Detection

2026-03-06

AI Summary

This work addresses the challenges of unstable detection and poor robustness of small objects in UAV aerial imagery, which arise from scale variation, detail degradation, and stringent computational constraints. To tackle these issues, the authors propose a lightweight collaborative detection framework that explicitly enhances fine-grained features prior to multi-scale fusion through a multi-backbone collaboration mechanism, cross-scale feature alignment, and a structure-preserving detail retention strategy. A unified localization-aware detection head is further introduced to improve spatial precision. The model architecture is co-optimized from three perspectives—image processing, channel design, and model lightweighting—achieving a balance between efficient inference and detailed perception. Without increasing deployment overhead, the proposed method significantly enhances the localization stability and detection robustness of small targets.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Hardware-aware ML
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• small object detection
• UAV imagery
• scale variation
• structural detail degradation
• computational constraints

Innovation

• Structural Detail Preservation
• Cross-Path Feature Alignment
• Localization-Aware Lightweight Design
• Multi-Scale Fusion
• Small Object Detection