YOLOv3 Smoking Detection Algorithm Fused with Transposed Convolution
To prevent safety incidents caused by smoking in public places,an improved smoking detection algorithm based on YOLOv3 framework is proposed.Firstly,to address the missing pixels in traditional upsampling,a convolution module with convolu-tional transpose for the replacement is designed,In the feature fusion part,the coordinate attention mechanism is added to make the network better focus on small targets.The improved k-means++is used to optimize the prior box.Finally,the generalized intersec-tion over union(GIoU)is replaced with the intersection over union(IoU),it is taken as the loss function of the algorithm to further improve the detection accuracy.In addition,the multi-scene smoking dataset is constructed to achieve the data augmentation and ex-pansion on the dataset.Experimental results show that compared to the original algorithm,the improved algorithm increases the AP@0.5 and AP@0.5∶0.95 by 5.58％and 3.34％,respectively,and the frames per second(FPS)decreases by about 3 points.
deep learningtarget detectionsmall targetsmokingtransposed convolutionattention mechanism
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