Smoky Vehicle Detection Algorithm Based on Improved YOLOv5s Research
An improved YOLOv5s algorithm of smoky vehicle detection has been established to address the issue of low resolution and too small target to test.Firstly,the smoke vehicle dataset is constructed using public network data and real road photography to solve the problem of a limited dataset.Secondly,the introduction of Coordinate has further optimized the model.In addition,the paper introduces Coordinate and improves the regression loss function of the bounding box into GIOU to improve the location precision of the bounding box.Experiments show that the proposed model is effective in detecting small targets over a long distance,and it can solve the problem of false negative and false alarm.A comparison with the original YOLOv5s model reveals an increase of 3.1％in the average detection accuracy(mAP)and a 4.9％enhancement in the detection accuracy of the black smoke category(AP),the model exhibits strong generalization ability in small target scenarios.
Deep learningYOLOv5sSmoke detectionSmall target detectionAtmospheric environment monitoring
NETL
NSTL
万方数据
