Lightweight fog gun detection network embedded with attention mechanism
In response to the multiple incidents of fog cannon vehicles spraying in violation of regulations at automated air monitoring stations in China,the lack of a data set for fog cannon vehicles,the difficulty in labeling the various shapes of fog sprayed by fog cannon vehicles,the high real-time monitoring demands,and the need for high accuracy,this study established a spray data set for fog cannon vehicles.A method for annotating fog was designed,and a lightweight fog cannon vehicle detection network embedded with an attention mechanism was proposed,based on the YOLOv5 network.Firstly,the anchor box that was most suitable for the task was calculated using K-means++.Secondly,an attention mechanism(CA)module was embedded to enhance the feature extraction capability of the network.The Conv at the Neck was then modified to GSConv,and the C3 module was replaced with the GSC3 module,reducing the model parameters.Finally,NMS was replaced with Soft NMS to reduce the miss rate and enhance the stability of detection.The experimental results showed that compared to other annotation methods,the proposed annotation method increased the overall mAP by 13％.The parameter volume of the proposed network was only 83％of YOLOv5s and achieved an mAP of 67.8％.Compared with the mainstream target detection network,the proposed network reduced the volume of parameters while maintaining an increase in accuracy and the speed of detection.
fog cannontarget detectionattention mechanismlightweightYOLOv5
万方数据
维普
