Research And System Implementation Of Dense Vehicle Detection Based On Deep Learning

In recent years,the number of automobiles in our country has increased rapidly,resulting in complex traffic conditions and vehicle congestion on roads all year round,which has adversely affected daily traffic.It is also difficult for the traffic management department to grasp the situation of the congestion scene in time.Therefore,real-time monitoring of vehicles in traffic congestion scenarios is of great practical significance.With the continuous development of deep learning,great progress has been made in object detection technology.Even objects that are heavily occluded or affected by environmental factors can still achieve great detection results.However,for dense objects,the existing object detection algorithms have poor detection results,and there are often false detections and missed detections.Therefore,researching effective dense object detection algorithms is still a challenging task.Starting from the distribution characteristics of dense vehicle objects,an algorithm model that can optimize the vehicle detection ability when vehicles are congested is designed.At the same time,considering the limited ability of general object detection algorithms to detect vehicles in congested scenes,a cross-scene detection model that integrates accurate detection of common highway conditions and congested road conditions is proposed.The main work is summarized as follows:(1)By researching and analyzing the distribution characteristics of dense vehicle objects in traffic scenes,aiming at the problem that the existing object detection methods have low detection accuracy for dense vehicle objects,a dense vehicle detection algorithm based on multi-level and multi-resolution networks is proposed.The algorithm adopts a multi-level and multi-resolution training strategy and generates a candidate window that is more in line with the data characteristics through the adaptive anchor point generation method of shape prior.It effectively captures the missing feature details due to the dense distribution of the object and realizes the accurate identification of the dense object.(2)By studying and analyzing the defects of cross-scene detection capability in general object detection algorithms,a cross-scene vehicle detection algorithm based on an adversarial multi-head distillation network is proposed,which realizes the cross-scene detection of the transition from ordinary highway scenes to congested vehicle scenes.The algorithm adopts the distillation strategy for knowledge transfer and utilizes the multi-head structure to supervise the network to learn the vehicle distribution characteristics of the new scene while maintaining the detection ability of the cross-scene model for the old scene.In addition,for the old scenarios,the sample generation module is used,so that the knowledge transfer can be carried out without accessing any actual data,which is highly flexible.In the model inference stage,an adaptive head recommendation module is used to automatically assign scenes to input images,optimizing the model’s cross-scene detection capabilities.(3)Aiming at the huge pressure on expressways brought about by the improvement of motorization levels,a set of expressway intelligent analysis systems is developed based on the proposed dense vehicle detection algorithm,so as to improve the efficiency of relevant departments in managing and controlling congestion.At the same time,the system adds functions such as abnormal event detection and early warning,and traffic flow statistics.The system achieves intelligent real-time monitoring of highway conditions.