基于改进的YOLOv5模型和射线法的车辆违停检测

Illegal parking detection based on improved YOLOv5 model and ray method

车辆违法停车将会降低道路通行效率,引发交通拥堵和交通事故.传统的车辆违停检测方法参数量大且准确度低.为此,本文提出了一种使用改进的YOLOv5模型和射线法的车辆违停检测方法.首先设计了轻量化的特征提取模块,减少模型参数量;其次在模型中加入注意力机制,从通道维度和空间维度增强模型的特征提取能力,保证模型精度;接着使用混合数据增强丰富数据集样本,提升复杂背景下的检测效果;然后选用EIoU作为损失函数提高模型定位能力.实验结果表明,改进后的模型均值平均精度达到91.35%,比原始YOLOv5s提升1.01个百分点,并且参数量减少35.79%.最后将改进后模型与射线法结合,在Jetson Xavier NX嵌入式平台的检测速度可以达到约28帧/s,能够实现实时检测.

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.