A New Vehicle Detection Framework Based on Feature-Guided in the Road Scene

Abstract

Vehicle detection plays a crucial role in the field of autonomous driving technology. However, directly applying deep learning-based object detection algorithms to complex road scene images often leads to subpar performance and slow inference speeds in vehicle detection. Achieving a balance between accuracy and detection speed is crucial for real-time object detection in real-world road scenes. This paper proposes a high-precision and fast vehicle detector called the feature-guided bidirectional pyramid network (FBPN). Firstly, to tackle challenges like vehicle occlusion and significant background interference, the efficient feature filtering module (EFFM) is introduced into the deep network, which amplifies the disparities between the features of the vehicle and the background. Secondly, the proposed global attention localization module (GALM) in the model neck effectively perceives the detailed position information of the target, improving both the accuracy and inference speed of the model. Finally, the detection accuracy of small-scale vehicles is further enhanced through the utilization of a four-layer feature pyramid structure. Experimental results show that FBPN achieves an average precision of 60.8% and 97.8% on the BDD100K and KITTI datasets, respectively, with inference speeds reaching 344.83 frames/s and 357.14 frames/s. FBPN demonstrates its effectiveness and superiority by striking a balance between detection accuracy and inference speed, outperforming several state-of-the-art methods.