Abstract:

For the hardware limitation in autonomous driving scenarios and the challenges faced by lightweight algorithms in detecting small target vehicles， a novel lightweight vehicle detection algorithm， RT-DETR-tiny， is proposed. Firstly， a new Redundant Graph Rapid Generation Module （ReduFast block） is proposed， which uses a cascade feature extraction structure to avoid the loss of small target feature information caused by redundant information， and reduce the computational redundancy. The lightweight network ReduFastNet， designed based on this module， serves as the feature extraction network， achieving faster inference speed compared to other lightweight networks. Then， during the feature fusion stage the DGSTM module is incorporated to further streamline the model， while the EAAIFI module is designed to ensure real-time performance during feature fusion. Finally， for the problem of boundary boxes being susceptible to noise in small target vehicle detection， the DIOU is introduced to optimize the original lossoriginal loss function， enhancing the accuracy of the target center positions and mitigating excessive penalties of the model caused by on aspect ratio fluctuations of the prediction box. Experimental results demonstrate that， on the BDD100K-Urban nighttime dataset， the proposed algorithm achieves a detection accuracy of 75.3%， with only a 0.1% loss， while parameters and computational load decreases by 37.1% and 33.5%， respectively， achieving a detection frame rate of 45.1 frames per second and enhancing detection speed by 5 percentage points. In comparison to other mainstream lightweight object detection models on the UA-DETRAC-Small Car dataset， RT-DETR-tiny balances high detection accuracy and minimal parameter count and computational load， outperforming similar object detection algorithm， which facilitates accuracy and edge deployment for real-time vehicle detection in autonomous driving contexts.

Key words: automatic driving, lightweight, vehicle target detection, RT-DETR