Common dish identification and detection based on YOLOv5s network
In order to solve the problem of manual settlement and low efficiency of canteen and restaurant dishes,several types of improved algorithms were proposed based on the YOLOv5s network model,among which the accuracy,recall and average accuracy of YOLOv5s,YOLOv5s-C2f,YOLOv5s-SE,YOLOv5s-MobileNetV3 and YOLOv5x algorithms were 83%,88.6%,89.4%;94.9%,79.1%,87.6%;91.6%,76%,84.9%;88.3%,94.9%,81.5%;93.6%,99.4%,99.4%,respectively;and the detection time of each image was 0.36,0.29,0.34,0.23,and 0.98 s,respectively.Experimental results show that compared with the YOLOv5s algorithm,the average accuracy of the YOLOv5s-MobileNetV3 algorithm is reduced by 7.9%,and the detection time is reduced by 36.12%.The average accuracy of the YOLOv5s-C2f algorithm is reduced by 1.8%,and the detection time is reduced by 19.44%.The average accuracy of the YOLOv5x algorithm is increased by 10%,and the detection time is increased by 63.27%.The YOLOv5s-MobileNetV3 algorithm maintains accuracy while greatly reducing the detection time,effectively achieving a balance between fast detection and performance.The YOLOv5x algorithm has a high accuracy rate and is suitable for applications where high accuracy is required.The research provides technical support for smart food services.
deep learningC2f modulechannel attention mechanismYOLOv5sdish identification
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