Illegal parking detection based on improved YOLOv5 model and ray method
Illegally parked vehicles reduce road traffic efficiency,and cause traffic congestion even traffic accidents.Traditional vehicle detection methods are perplexed by a large number of parameters and low accuracy.Here,we propose a method using the improved YOLOv5 model and ray method to detect illegally parked vehicles.First,a lightweight feature extraction module is designed to reduce the amount of model parameters.Second,the at-tention mechanism is added to the model to enhance its feature extraction ability from both channel dimension and spatial dimension to ensure the model's accuracy.Then,the mixed data is used to enhance and enrich the dataset samples thus improve the detection performance in complex backgrounds,and EIoU is selected as the loss function to improve the model's positioning performance.Experiments show that the mean accuracy of the improved YOLOv5 model reaches 91.35%,which is 1.01 percentage points higher than that of the original YOLOv5s,and the number of parameters is reduced by 35.79%.Finally,the improved YOLOv5 model is combined with the ray method,which can reach real-time inspection speed of 28 frames per second on the embedded platform of Jetson Xavier NX.
万方数据
维普
