Research And Application Of Intelligent Monitoring Method For Vehicle Parking Location

In the parking-lot,to supervise the event of illegal parking of vehicles often use the large screen real-time video display manual observation,but this method has a large workload and passive reliance on staff,easy to cause the problem of inadequate supervision.To this end,this thesis lakes the application of artificial intelligence technology in vehicle parking management as the entry point,combines the neural network model with the monitoring technology,researches,and gives a set of intelligent recognition methods that can identify different categories of vehicles illegally driving or parking in the no-parking area according to the designated area;at the same time,with the help of the researched method,realizes a vehicle intelligent recognition and parking that can be applied to the parking lot auxiliary management.The main research work and content of this thesis are summarized as follows:Ⅰ.The thesis gives the YOLOv5-AT vehicle intelligence recognition method for the disadvantages of YOLO network which is difficult to recognize tiny targets and does not adapt to long-distance camera images·The tiny target detection head and attention module are integrated into the YOLOv5 neural network structure,and the Bi-FPN structure is fused in the Neck part of the neural network to enhance the feature fusion capability of the network,improve the accuracy of the network recognition,make the neural network more adaptable to the images taken by the camera,and allow the network to learn faster and converge faster.Through network performance comparison experiments,it is proved that the YOLOv5-AT network has good performance and outperforms the traditional YOLO network structure in terms of accuracy.Ⅱ.For the problem of the slow speed of traditional dynamic target monitoring algorithms combined with a rieural network,the thesis proposes a DIOU dynamic target monitoring algorithm,The algorithm utilizes neural network prediction bounding boxes for dynamic targd filtering,which is fast and accurate and prevents invalid information from flooding the system screen,achieving the goal of making good use of neural networks in the parking lot environment.Through experiments,it is proved that the algorithm is faster and more accurate than the traditional dynamic target monitoring algorithm when used in combination with neural networks.Ⅲ.Designed and implemented a set of vehicle parking intelligent supervision software.By using this software,users can maintain a "no-go zone" and select the type of no-go zones.The user-defined no-go area will be passed to the vehicle location monitoring program after coordinate conversion,and the program will judge whether a vehicle enters the no-go area according to the coordinates.When a vehicle of the same type as the selected one drives into the no-go zone,the information of the offending vehicle is recorded in the system and an alert is given.Due to the presence of a static target filtering algorithm in the system,the vehicle will only be marked on the screen when it moves at a faster speed or drives into a prohibited area,reducing the risk of misjudgment and false alarm.In addition,users can adjust the intensity of static target filtering in conjunction with the actual situation to adapt to various different road traffic conditions.