Multi-drone system improves vehicle detection with joint association and recognition
CARNet: End-to-End Coupled Association-Recognition Network for Fine-Grained Multi-Drone Collaborative Detection
Computer Vision and Pattern Recognition
Summary
Detecting vehicles from multiple drone cameras is tricky because drones see the same vehicles from different angles and need to figure out which views match the same vehicle before identifying it. Existing methods do these in separate steps, which can cause errors if the matching step fails. The authors created CARNet, a system that combines matching and recognizing vehicles together, improving accuracy even when many similar vehicles appear close together. They also built a new detailed dataset to test this kind of multi-drone vehicle detection.
What this means in practice
- •For drone system developers: Improve multi-drone vehicle detection systems by integrating target matching and recognition for better accuracy in crowded scenes.
- •For traffic monitoring teams: Enhance remote vehicle identification using collaborative aerial views for fine-grained category recognition in urban environments.
Authors
Zhao Zhang, Ruigang Fu, Yu Zhang, Jiajun Xiang, Zixuan Xiao, Ping Zhong
Abstract
Multi-drone collaborative detection aims to enhance target recognition by leveraging complementary observations from overlapping aerial viewpoints. Existing approaches typically follow a two-stage pipeline that first establishes cross-view correspondences and then performs fusion-based recognition. However, this decomposition suffers from inconsistent optimization objectives, redundant pairwise modeling, and irreversible error propagation, where incorrect associations inevitably undermine subsequent recognition performance. To overcome these limitations, we propose the Coupled Association-Recognition Network (CARNet), a unified framework that jointly optimizes cross-view target association and recognition fusion through a shared target-pair representation. To improve association reliability in densely populated scenes with visually similar targets, CARNet explicitly incorporates spatial cues into pairwise relation modeling. Moreover, we introduce two complementary causal-inspired intervention strategies: the first enhances cross-view association by emphasizing appearance-confusing pairs, while the second bolsters the robustness of fusion-based recognition by learning stable multi-view representations under incomplete visual evidence. In addition, we present MDMT-CD, a fine-grained extension of the MDMT dataset that enriches the original vehicle annotations with detailed category labels, providing the first benchmark specifically designed for multi-drone collaborative vehicle detection. Extensive experiments demonstrate that CARNet consistently outperforms existing state-of-the-art methods. The MDMT-CD dataset and source code will be available at https://github.com/bigdreaming11/CARNet.