Research On Object Detection And Image Segmentation Based On Deep Learning In Traffic Scene

With the continuous advancement of computer technology,the artificial intelligence based on deep learning develops rapidly and has been widely applied in trasportation scenarios.Especially in the scenarios of Intelligent Transportation System and Automatic Driving,by using object detection algorithm to detect the collected videos,it can obtain not only the basic information of vehicles and pedestrians,but also the corresponding traffic flow information,and movement trend can be predicted.In addition,we also need to accurately perceive the surrounding environment.Therefore,image segmentation technology are used to accurately obtain surrounding environment information from road images to distinguish pedestrians,vehicles,obstacles,lane lines and driving areas and other important information.Traditional methods of object detection and image segmentation need to design features manually,which seriously affects the performance of detection and segmentation.The object detection and image segmentation method based on deep learning extract richer feature information by deepening the level of neural networks,which can significantly improve the performance of detection and segmentation.In traffic scene,vehicles are one of the most important targets,but it is difficult to extract features from the detection network due to their own and environmental characteristics,and problems such as missed detection,false detection and low detection accuracy often occur.In this thesis,aiming at the traffic scenes,the vehicle detection and image segmentation are taken as research task.On the basis of the mature deep learning network,some effective improvement methods are proposed.Specific research work is as follows:(1)A vehicle detection algorithm based on improved YOLOv4 is proposed.Aiming at the problem of insufficient feature extraction in vehicle detection by YOLOv4 object detection algorithm,the YOLOv4 detection network is improved by introducing the ECA attention mechanism and the high resolution network HRNet,which significantly improves the feature extraction ability of the network.The experimental results show that the improved method proposed in this thesis can effectively improve the problem of missed detection and false detection in vehicle detection of YOLOv4.It improves the detection accuracy while ensuring the real-time detection.(2)An image segmentation algorithm based on improved Deep Labv3+ is proposed.In the task of image segmentation in traffic scene,in order to solve the problem of excessive loss of image information caused by traditional pooling operation,the Softpool pooling method is used to replace the original pooling operation to improve the network.In addition,aiming at the problem that the slow running speed of image segmentation algorithm cannot meet the requirements of real-time segmentation,Mobile Net V2 is used as the backbone feature extraction network of Deep Labv3+.Finally,the experimental results show that the proposed method improves the accuracy of image segmentation while ensuring the segmentation speed,and effectively improves the segmentation effect.