Research on an optimized MobileNet model for molybdenum ore recognition

Given the current situation where most of the tailing screening in mines is manual selection by workers, this paper proposed a deep learning molybdenum ore recognition method based on an optimized MobileNetV2 model, which would improve the recognition accuracy and efficiency of molybdenum ore in gray-scale images obtained under X-ray irradiation. A self-labeled molybdenum ore gray-scale image data set was constructed, and the images were preprocessed and normalized. Based on the MobileNetV2 architecture, innovations and improvements were made by introducing the coordinate attention mechanism (Coordinate Attention, CA). Adjusting the width factor and L2 regularization parameters enhanced the model’s feature extraction ability and generalization ability while reducing the training time. The experimental results showed that compared with the original MobileNetV2 model, the accuracy of this method in the molybdenum ore recognition task was increased by 3.5%. At the same time, the training time was significantly reduced. Compared with several typical convolutional neural network architectures such as ResNet50, EfficientNetB0, and VGG16, this model showed significant advantages in multiple key indicators such as accuracy, number of parameters, and training time.