Small-target smoking detection algorithm based on improved YOLOv5

Abstract

The use of general target detection algorithms for small-target detection is computationally costly and has a high missed detection rate. A lightweight small-target detection model based on YOLOv5 is proposed to address this issue.First, a maximum pooling layer is introduced to reduce the number of calculations. Second, Shuffle_Conv is designed to replace the ordinary convolutional layer to reduce model parameters. To further compress the model, the Add fusion method is used in the C3 module, while the GAC3 layer is designed with GhostNet. Finally, Mosaic_9 is introduced to improve the small-target detection without increasing the number of calculations. Compared with YOLOv5, computation and parameters of the improved model are reduced by 84.9% and 39.1%, respectively, and the accuracy is improved by 2%, which is more obvious than that of the original model.