A YOLO-based Federated Learning Network for Container Lock Pin Classification
The task of automated container lock pin disassembly is the last technical bottleneck in realizing unmanned docks.The real-time results of lock pin image classification are used for succeeding mechanical arms to initiate the adapted disassembly device and action program,which is the key link in the automated disassembly task.A rich and diverse lock pin dataset is conducive to ensuring the robustness of the lock pin classification neural network.However,due to the commercial sensitivity of lock pin usage and excessive trans-mission cost of images,lock pin users such as docks do not usually share their own lock pin image data with others,but iterate their own neural network models.This leads to the fact that each user's own model is less accurate in the classification of previously rare lock pins,which is highly susceptible to failure and has an impact on the efficiency of unmanned operations.Since traditional center-based learning cannot solve the problem above,we propose a YOLO-based federated learning network for lock pin classification.Firstly,a lock pin classification neural network is established on the basis of YOLOv8.Secondly,a distributed federated learning architecture is established based on Flower framework,and an improved algorithm,which is called FedAvg-mAP,is proposed to improve the performance of the aggregated global model.In addition,an early stopping strategy is introduced in the local model training phase,which accelerates the convergence of the global model and makes the convergence process smoother.Experiments show that the YOLO-based federated learning architecture for lock pin classification proposed can realize the function of traditional centralized learning without image data sharing,and the improved FedAvg-mAP algorithm has better performance over the traditional algorithm.
万方数据
维普
