Vehicle detection based on improved YOLOv4

Abstract

With the development of deep learning, the performance of vehicle detection algorithms based on deep learning is constantly improved, which plays an important role in the construction of intelligent transportation. Single-stage target detection model is widely used in vehicle real-time detection because of its advantages of detection speed. In view of the low detection rate of small objects in images, this article proposes a vehicle object detection method based on the improved YOLOv4 algorithm, using k-means clustering algorithm to re-create a anchors suitable for the UA-Detrac dataset and improve the PANet. Compared with other target detection methods, the improved algorithm can effectively detect small targets and improve the Precision, Recall and mAP of vehicle targets.