基于改进MobileNet网络的多类别垃圾分类算法OA

view of the large amount of garbage and the fact that a picture contains multiple garbage objects,this study proposes a garbage detection and classification algorithm based on the improved MobileNet network,which in-tegrates the MobileNet network into YOLOv5(You Only Look Oncev5)target detection algorithm.At the same time,the CBAM(Convolutional Block Attention Modul)module is introduced in the backbone to filter meaningful informa-tion,and the vision transformer is used to aggregate and form image features.In addition,the weighted bidirectional feature pyramid network is used to distinguish the contribution of different features.At the same time,the ECA(Effi-cient Channel Attention)module is introduced to combine the image features and transmit them to the prediction lay-er.Finally,in order to obtain better performance when there is occlusion between garbage targets,soft-NMS(soft-Non Maximum Suppression)method and Alpha-IoU(Alpha-Intersection over Union)loss function is used to pre-dict the extracted features.The experimental results show that the method proposed in this study can realize the loca-tion and recognition of multi-target and multi-category garbage.,and the mAP(mean Average Percision)value reaches 90.31%,which is 4.95%higher than that of YOLOv5 network,and the processing speed is shortened by a-bout 2.4 seconds.Compared with the Faster R-CNN(Region-based Convolutional Neural Network)algorithm which integrates ResNet(Residual Network)network,the algorithm proposed in this study improves the processing efficiency on the premise of ensuring the accuracy.