针对在夜间场景下对车辆的一些异常检测效果不佳、模型参数量大等问题,制作了夜间车辆异常数据集,并对YOLOv5进行改进,将夜间中车辆异常图像放入其中训练,最终检测效果有所提升.其中改进有:在YOLOv5网络模型中Backbone层引入深度超参数卷积DO-DConv,可以加快收敛速度、提高网络性能、有效降低参数量和浮点计算量并提高检测精度;根据轻量化卷积GSConv可以在检测的准确性和速度之间实现平衡,设计了新的VOV_SimAM模块以及C3_SimAM模块,以提高对重要异常特征的提取能力;提出流对齐交叉融合特征金字塔网络(flow alignment feature pyramid networks,FaFPN)和FaConcat拼接方式,减少参数量与浮点运算量;引入理智交并比(wise-intersection over union,Wise-IoU)损失函数以解决质量较好与质量较差的样本平衡问题,实现对车辆碰撞、车辆起火和车辆翻车的检测.该模型相较原YOLOv5网络模型,参数量和浮点计算量分别减少了 8.5％和28.8％,对车辆碰撞、车辆起火和车辆翻车的检测平均精度(average precision,AP)值分别提升了 11.3％、5.9％、29.1％,均值平均精度(mean average precision,mAP)值提升了 15.4％,为智能视频警务应用提供了 一个较好的借鉴.
Vehicle Anomaly Detection in Night Based on FaFPN
In order to solve the problems of poor detection effect of some abnormal vehicles in night scenes and large amount of model parameters,a night vehicle abnormal dataset was made and YOLOv5 was improved.The abnormal images of vehicles in the night were put into the training,and the final detection effect was improved.The improvements include:in the Backbone layer of YOLOv5 network model,the extended convolution depthwise over-parameterized convolutional with deep overparameterized convolution was in-troduced,which could accelerate the convergence speed,improve the network performance,effectively reduce the amount of parameters and floating point calculation and improve the detection accuracy.according to the lightweight convolution GSConv,which could achieve a balance between the accuracy and speed of detection,the new VOV_SimAM module and C3_SimAM module were designed to improve the extraction ability of important abnormal features.The flow alignment feature pyramid networks cross-fusion pyramid struc-ture and FaConcat splicing method was proposed to reduce the amount of parameters and floating point calculation.The Wise-Intersec-tion over Union Wise-IoU loss function was introduced to solve the balance problem of good and poor quality samples,and the detection of vehicle collision,vehicle fire and vehicle overturning was realized.Compared with the original YOLOv5 network model,the amount of parameters and floating point calculations are reduced by 8.5％and 28.8％,respectively.The detection average precision AP values of vehicle collision,vehicle fire and vehicle overturning are increased by 11.3％,5.9％and 29.1％,respectively,and the mean average precision mAP value is increased by 15.4％,providing a good reference for intelligent video policing applications.
vehicle anomaly detectionYOLOv5feature fusionattention mechanismloss function
万方数据
维普
