关键词: 目标检测, 全景交通监控, [K]-means, 特征融合, 卷积神经网络

Abstract:

Aiming at the complex traffic scenes, vehicles and pedestrians, and the large scale changes, an improved YOLOv3 panoramic traffic monitoring multi-target detection method is proposed. Firstly, based on the YOLOv3 network, four detection scales are designed with consideration of the size and scale target characteristics, and multi-scale feature fusion processing is performed. Then [K]-means clustering method is used to cluster the labeling target box in the dataset, and the width and height of optimized clustering anchor box are selected as the initial candidate box. The panoramic traffic monitoring targets include five categories：large vehicle, small vehicle, motorcycles, bicycles and pedestrians. The average accuracy and recall rate of target detection on the test set reaches 84.49% and 97.18%, respectively, which is 7.76% and 4.89% higher than the original YOLOv3. The processing speed can meet the real-time detection requirements in traffic scenarios.

Key words: target detection, panoramic traffic monitoring, [K]-means, feature fusion, convolutional neural network