Multi-Target Detection Method of Intelligent Driving Traffic Scene Based on Faster R-CNN++

The current multi-objective detection methods are plagued by several issues, including insufficient detection efficiency, slow speeds, and difficulties in deciphering driving rules. To address these challenges, we introduce a traffic scene classification method that utilizes Improved Faster Regions with Convolutional Neural Network features (Faster R-CNN++). Initially, the shared convolutional layer and the Recurrent Criss-Cross Attention (RCCA) layer are employed to extract features from the input image. Subsequently, the derived feature map is fed into the Region Proposal Network (RPN) to generate detection boxes, and the Region of Interest Align (RoI Align) layer selects features corresponding to each Region of Interest (RoI) on the feature map, guided by the RPN’s output. Ultimately, we adopt an alternating training approach. Simulation experiments confirm the effectiveness of our method in multi-scene object detection. Our method has demonstrated significant improvements over existing algorithms and has delivered outstanding performance on the BDD-100[Formula: see text]K, ApolloScape, and NuScenes datasets. The results indicate that our deep learning-based traffic scene classification method can accurately discern the behavioral characteristics of various traffic participants.