Detection and tracking of small-sized vehicles in aerial video from UAVs
TL;DR
An enhanced tracking-by-detection framework integrating an improved YOLOX with DeepSORT to mitigate the aforementioned challenges is established, and the Convolutional Block Attention Module (CBAM) into the feature fusion stage optimizes feature extraction within complex environments.
Abstract
Vehicle detection and tracking in unmanned aerial vehicle (UAV) imagery, while critical for intelligent transportation systems, remain challenging due to high omission rates, false alarms, and frequent identity switches among small-sized vehicles. The proposed research establishes an enhanced tracking-by-detection framework integrating an improved YOLOX with DeepSORT to mitigate the aforementioned challenges. Integrating the Convolutional Block Attention Module (CBAM) into the feature fusion stage optimizes feature extraction within complex environments. Furthermore, the detection head is refined by replacing standard Binary Cross-Entropy (BCE) and IoU losses with Varifocal Loss and Enhanced IoU (EIoU) loss, respectively, to mitigate sample imbalance and boost localization accuracy. A sliding-window-based image slicing method is also introduced to enhance detection sensitivity to small-sized spatial features. Experimental results demonstrate that the proposed method significantly reduces detection errors and identity switches while strengthening tracking stability.