Visual detection method of tobacco moth in cigarette factory based on improved lightweight YOLOv5s.

To address the problems of slow detection speed and low accuracy commonly found in cigarette factory warehouse workshops when detecting tobacco moth, a visual detection method for tobacco moth in cigarette factories based on improved lightweight YOLOv5s was developed. The method utilizes the correlation and redundancy between feature maps to design the EESP-Ghost module, and uses this module as the basis for designing a double-attention Ghost-bneck block incorporating an efficient spatial pyramid, which is introduced into the YOLOv5s model to achieve lightweighting of the deep neural network model while improving the detection accuracy. The method is used for validation experiments on the tobacco moth dataset. The results showed that the method improved the average accuracy by 4.37% with only 49.88% of the original YOLOv5s parameter count. When the tobacco moth adhering to the sticky board was detected in a real detection scenario, the method has high detection confidence and correct detection number, which could realize the high-precision real-time detection of the tobacco moth in the cigarette factory, and provide a guarantee for the effective control of the tobacco moth. [ABSTRACT FROM AUTHOR]