Research on Defect Detection of Food Packaging Bags Based on Improved Yolov5
Aiming at the low efficiency of traditional artificial detection of food packaging bag defects,a food packaging bag defect detection method with improved YOLOv5 model was proposed.SE attention mechanism was added to improve the detection ability of the model for small defects.The CARAFE upsampling operator is introduced to improve the quality of reconstruction by using the content information of feature graph.Replace the activation function with a Mish activation function to enhance the accuracy and generalization of the network.On the self-made cookie bag data set,the average accuracy of the improved network is 88.4%,the mAP of the final detection model is increased by 21.76%,and the number of parameters is decreased by 9.36%compared with the original model.
deep learningYOLOv5defect detectionfood packaging bag
万方数据
维普
