Automatic Detection of Vehicle Targets Based on CenterNet Model
Yifan Sun, Zhongzhi Li, Lang Wang, Jiankai Zuo, Lang Xu, Mi Li · 2021
In the context of the new era, the concept of smart transportation has appeared in people's lives. Detecting vehicles and pedestrians has become a popular application research direction in the field of target detection. Aiming at the problem that traditional methods have low accuracy in vehicle detection in the actual environment, a vehicle detection method based on the CenterNet model in deep learning is proposed. When constructing the model, this paper regards the target as a point- the center point of the target BBox. The detector uses key point estimation to find the center point and returns to other target attributes, such as size, 3D position, direction, and even pose. This paper uses Peking University/Baidu_Autonomous Driving dataset for training and testing. The experimental results show that compared with Inception-ResNet-V2 and Efficient-Det, the method proposed in this paper has significantly improved the average detection accuracy. It has a good detection effect for vehicles in actual scenes, and the network has certain robustness.