Optimization Study of Ore Grade Recognition Algorithm Based on Attention-enhanced YOLOv5l
At the current stage,traditional chemical analysis methods for grade determination have been found to be time-consuming and laborious.Similarly,image recognition for grade analysis of bulk ore suffers from significant shape interference.To address these issues,an image recognition method for ore and ore powder features based on YOLOv5 is proposed.During the training process,the Convolutional Block Attention Module(CBAM)and the Squeeze and Excitation module(SENet)into the neural network to enhance the model's ability to learn specific features of ore powder has been incorporated.Significant details in the ore powder were focused towards by the attention mechanism while ignoring irrelevant information,thereby improving rec-ognition accuracy.Additionally,the loss function to enhance its classification effect,and investigate the impact of the loss func-tion on the effectiveness of ore powder recognition were modified.The study results show that in the grade recognition of iron ore powder,the network model with the added CBAM attention module has a training accuracy of 86%,and the network model using the SENet attention module has a training accuracy of 80%.Both are slightly higher than the original model's accuracy of 79%.However,the network model with the adjusted loss function saw a decrease in training accuracy by 5%.The YOLOv5l+CBAM model with a loss function set to 0.5 is optimal was concluded.The study results demonstrate that the proposed image recognition method for ore powder features has certain applicability.
万方数据
维普
