RSOD: Real-time small object detection algorithm in UAV-based traffic monitoring

The prevailing applications of Unmanned Aerial Vehicles (UAVs) in transportation systems promote the development of object detection methods to collect real-time traffic information through UAVs. However, due to the small size and high density of objects from the aerial perspective, most existing algorithms are difficult to accurately process and extract informative features from the traffic images collected by UAVs. To address the challenges, this paper proposes a new real-time small object detection (RSOD) algorithm based on YOLOv3, which improves the small object detection accuracy by (i) using feature maps of a shallower layer containing more fine-grained information for location prediction; (ii) fusing local and global features of shallow and deep feature maps in Feature Pyramid Network(FPN) to enhance the ability to extract more representative features; (iii)assigning weights to output features of FPN and fusing them adaptively; and(iv) improving the excitation layer in Squeeze-and-Excitation attention mechanism to adjust the feature responses of each channel more precisely. Experimental results show that, when the input size is 608 × 608 × 3, the precision of the proposed RSOD algorithm measured by mAP@0.5 is 43.3% and 52.7% on the Visdrone-DET2018 and UAVDT datasets, which is 3.4% and 5.1% higher than those of YOLOv3, respectively.