Research on night target detection based on improved YOLOv5 model
To solve the problem of low accuracy in practical application of video surveillance target detection at night,here an improved yolov5 is proposed.A dataset of real night scene is established,this dataset was put into the improved YOLOv5 for train-ing,the purpose of detecting targets at night was achieved.To improve the clustering efficiency of target samples at night,in the im-proved YOLOv5,the K-means++ clustering algorithm is utilized to generate adaptive anchor boxes.The improved CBAM attention mechanism was put into the CSP_X module to get important features of the target at night.Replace Bottleneck with the GSBottle-neck module,which is to reduce the computation and parameters of the network model.By experimental results,the mAP values af-ter training by YOLOv5 and improved YOLOv5 were 86.69%and 91.98%,respectively,after training with improved YOLOv5,the AP values of motor vehicles,non-motor vehicles and license plates increased by 2.00,6.66 and 7.19 percentage,respectively.The improved YOLOv5 can provide better technical support for detecting vehicle characteristics at night.
target detectionYOLOv5target at nightclustering algorithmlightweightattention mechanism
万方数据
维普
