Smoking Detection at Construction Site Based on Improved YOLOv5s
Smoking at construction sites and other complex construction sites may cause fire,explosion and other accidents,seriously en-dangering construction safety.In order to achieve smoking detection at construction sites and other construction sites,we use YOLOv5s to detect faces and cigarettes,and judge whether there is smoking behavior at the construction site according to the position relationship between faces and cigarettes.In order to improve the detection accuracy of faces and cigarettes,we make three improvements on the basis of the original model.First,the dynamic label assignment method,SIMOTA,is adopted,which improves the network recall rate and detection speed.Second,the scale-in feature interaction module,AIFI,is introduced,which enhances the network feature expression ability.Third,dynamic convolution,ODConv,is used to optimize the C3 feature extraction module,which improves the network accuracy.Experiments on self-made data sets show that the improved network has improved by more than 2%in accuracy,recall rate and average precision,and the detection speed has increased by 22%,achieving obvious performance advantages.Compared with the ma-instream algorithms,the improved algorithm has obvious advantages in detection speed and network performance,meeting the needs of smoking detection at construction sites.
construction sitecigarette detectionobject detectionYOLOattention mechanism
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