基于目标检测和语义分割共享单车类别与违停检测.

At present, the detection of road violation event is based on the artificially selected area under the fixed camera for object detection. However, the artificially selection area has a heavy workload, and as the camera rotates, these area will be invalid. Aiming at this problem, this paper first proposed a method of violation detection combining object detection and semantic segmentation. Firstly, this paper used the transfer learning, multi-stage training schedule to train the Faster R-CNN model to extract the categories of the shared bicycles and the bounding boxes position information. Then this paper used group normalization to modify the semantic segmentation DeepLab v3 + network model, improved the model precision of training under small batch size, and obtained the semantic and regional information of the road scene. Finally combining two parts of the information to determine whether the sharing bicycle was illegally parked according to the proportion of different road areas in the bicycle detection bounding boxes. The experimental results show that the mAP of the sharing bicycle detection is 72. 36%, and detection accuracy of the sharing bicycles illegal parking is 89. 11 %, which can be applied to the actual road monitoring environment. [ABSTRACT FROM AUTHOR]