Office Smoking Behavior Detection Based on Improved YOLOv5 Model
To solve the problems of low accuracy and easy false detection of small targets in current smoking behavior detection,an improved YOLOv5 recognition model YOLOv5s+is proposed.This model combines the backbone network of YOLOv5 with BoTNet to improve the feature extraction ability of the model,enabling it to detect smaller target objects;At the same time,the feature fusion part is improved by applying a weighted bidirectional feature pyramid BiFPN in the neck of the network model to efficiently fuse shallow position information and deep high-level semantic information,effectively improving detection accuracy.Integrate publicly available online datasets and self-made datasets into an office smoking experimental dataset,and compare the detection performance of the YOLOv5s+model with the original YOLOv5 model on this dataset.The experimental results show that the average accuracy(mAP)of the improved model YOLOv5s+is 81.8%,with an accuracy of 82.8%and a recall rate of 83.9%.Compared with the original model,it has improved by 5.4%,4.1%,and 6.4%,respectively,and has achieved good detection of office smoking behavior.
deep learningYOLOv5smoking detectionfeature fusion