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.
关键词
深度学习/YOLOv5/吸烟检测/特征融合
Key words
deep learning/YOLOv5/smoking detection/feature fusion
维普
万方数据