Abstract:

To address the challenges posed by complex traffic scenarios, particularly congested roads where traffic objects are densely packed and often occlude each other and small-scale objects are detected inaccurately, a new object detection model called YOLO-T (You Only Look Once-Transformer) is proposed. First, the CTNet backbone network is introduced, which has a deeper network structure and multiscale feature extraction module compared with CSPDarknet53. Not only can it better learn the multilevel features of dense objects but can also improve the model’s ability to handle complex traffic scenarios. Moreover, it directs the model’s focus toward the feature information of small objects, thereby improving the detection performance for small-scale objects. Second, Vit-Block is incorporated, which integrates more features by parallelly combining convolution and Transformer. This approach balances the relevance of local and contextual information, thereby enhancing detection accuracy. Finally, the Reasonable module is added after the Neck network, introducing attention mechanisms to further improve the robustness of the object detection algorithm against complex scenarios and occluded objects. Experimental results indicate that compared with baseline algorithms, YOLO-T achieves a 1.92% and 12.78% increase in detection accuracy on the KITTI and BDD100K datasets, respectively. This enhancement effectively boosts detection performance in complex traffic scenarios and can assist drivers to better predict the behaviors of other vehicles, thus reducing the occurrence of traffic accidents.

Key words: intelligent transportation, deep learning, object detection, YOLO, complex traffic scenarios