Model With Leader-Follower Backbone and Bifurcation Fusion for UAV Traffic Object Detection

The application of Unmanned Aerial Vehicles (UAVs) is crucial in traffic information collection. In addressing the challenge of detecting small targets in UAV imagery, simply increasing the model depth is not the most optimal solution. In this work, we propose MSDet, a novel object detection method based on master-slave backbone and bifurcation fusion. Different feature extraction methods provide varying feature information, and their fusion enables a more comprehensive and multidimensional description of the target. The simplified auxiliary networks are connected layer by layer with the main backbone, and their final output is fed back to the initial feature map. The main backbone and auxiliary networks can be flexibly selected and combined to adapt to the unique features of different scenarios. Bifurcation fusion achieves flexible multi-scale feature fusion by introducing branches during the top-down fusion process. One branch performs deeper top-down fusion to capture more shallow features, whereas the opposing branch offers a comprehensive understanding of the overall structure. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods when applied to three UAV datasets. Furthermore, this study suggests that integrating with different backbones may yield better performance than simply scaling up models when faced with challenging situations.