Abstract:  In vehicle recognition, due to different shooting angles and distances, the size of the imaged vehicle is smaller and the vehicle has different degrees of occlusion, resulting in detection error and missed detection. In order to solve this problem, based on the single stage target detection network YOLOv4(You Only Look Once version 4) algorithm, a recursive YOLOv4 target detection algorithm is proposed based on attention mechanism, namely RC-YOLOv4 algorithm. In order to improve the detection capability of the algorithm for small size vehicles after imaging, the CBAM ( Convolutional Block Attention Module ) module is added to YOLOv4 algorithm. This module combines the channel and spatial attention mechanism, which can help the network model pay more attention to the key information and small target information in the detected image. For the detection of partial occlusion of vehicles, a RFP(Recursive Feature Pyramid) structure is adopted to enhance the model’s ability to extract deep feature information. The RFP structure is similar to the human visual perception that selectively enhances or inhibits the activation of neurons. The features extracted from the backbone network are recursively fused and then fed back to the backbone network. Multiple feature fusion improves the network’s ability to extract and integrate contextual semantic information. It improves the detection accuracy of occluded vehicles. The experimental results show that the average precision of RC-YOLOv4(Recursive and CBAM You Only Look Once version 4 ) algorithm is 12. 69% higher than YOLOv4 algorithm on the self-made vehicle detection data set, and the detection speed can also meet the real-time requirements.

Key words: electronic information')">

electronic information, small object detection, occlusion detection, YOLOv4 algorithm, attention mechanism