Identification of Listeria monocytogenes based on CBAM1,2,3-YOLOv7
Foodborne pathogenic bacteria is one of the main pathogenic bacteria that threaten human health in refrigerated food,and it is a must-check item in food hygiene microbiology inspection.The traditional method of bacterial identification needs to be observe and counte by naked eyes after bacterial culture for national standard time,which is time-consuming and laborious because the observer is easy to get wrong counting due to eye fatigue.Moreover,traditional methods require special reagents for bacterial detection,which is costly and requires specialized knowledge to operate.To quickly and accurately detect small bacterial targets,this article proposed a new method for detecting foodborne pathogens-CBAM1,2,3-YOLOv7.Firstly,an industrial camera was used to replace a microscope to capture images,and the captured images of Listeria monocytogenes were inputted into the optimized model for recognition.This model added CBAM attention mechanism to the YOLOv7 model,making the model more sensitive in the channel domain and enhancing feature extraction ability.To enhance contrast,training was conducted on deep learning models,including Faster RCNN,YOLOv4,YOLOv5,and YOLOv7.Compared with YOLOv7 model,the optimized model improved the accurate mean value by 0.52%and the recall rate by 0.27%.The results suggested that CBAM1,2,3-YOLOv7 algorithm realized the high-precision identification of Listeria monocytogenes,which has guiding significance and reference value for the rapid detection of other foodborne pathogens.
Deep learningTarget detectionYOLOFaster-RCNNFoodborne pathogenic bacteriaMechanism of attention
NETL
NSTL
万方数据
