PHAM-YOLO Network-based Detection Method of Combustion Line of Cigarette Paper
To determine the combustion line of cigarette paper,the dataset for cigarette combustion line detection was construct from common scenarios.To address the challenges of detecting complex backgrounds,multiple targets,varying scales,and shapes of combustion lines,a PHAM(parallel hybrid attention mechanism)was embedded into the YOLO v5(you only look once,version 5)backbone network,and PHAM-YOLO was constructed for detecting multiple targets with varying scales and shapes in complex backgrounds.In addition,a spatial pyramid pooling fast(SPPF),the boundary box regression(BBR)module were introduced to improve the accuracy of combustion line positioning.The results showed that the proposed PHAM-YOLO network achieved the average precision mean(mAP),precision(P)and recall(R)of 99.0％,99.8％,and 99.0％,respectively,where mAP was improved by 5.0％compared to the original model and higher than other types of target detection methods.
cigarette papercombustion line detectionYOLOparallel hybrid attention mechanism
万方数据
维普
