MobileNet-PAS: A Lightweight Deep Learning Model for Non-Destructive Grade Identification of Pilose Antler Slices

Pilose antler is a valuable traditional Chinese medicinal herb. Slices of different grades show great disparities in medicinal potency and market price. Conventional visual discrimination of wax slices, powder slices, gauze slices and bone slices relies entirely on human inspectors. Such manual evaluation heavily depends on personal expertise and brings prominent subjective bias. Especially for gauze slices and powder slices with highly similar visual appearances, misclassification frequently occurs, making it hard to satisfy practical requirements for large-scale rapid inspection. Accordingly, this paper develops a modified lightweight convolutional network named MobileNet-PAS to realize intelligent grading of pilose antler slices. Built upon the MobileNetV4-small backbone, the proposed model reconstructs the dimensionality-reduction convolution inside UIB blocks using group convolution. The ConvBN layers following the last UIB block are replaced with partial convolution. A SimAM attention module is embedded at the rear of the UIB blocks to boost fine-grained feature representation. The original ReLU6 is further substituted with the PReLU activation function. Meanwhile, a Gap-Aware dynamic learning rate strategy is adopted to optimize parameter updating, so as to strengthen feature distinguishability while restraining model complexity. Experimental results demonstrate that MobileNet-PAS achieves a test-set accuracy of 96.03%, which outperforms the baseline by 3.31 percentage points with reduced parameters and computational cost. Grad-CAM visualization reveals that the improved network suppresses irrelevant background noise and focuses on intrinsic discriminative cues including osseous structure and texture within slices, yielding better classification performance for easily confused samples. Balancing prediction accuracy and lightweight inference performance, the presented approach offers a feasible algorithm for non-destructive rapid identification of pilose antler slices and delivers references for intelligent quality assessment of Chinese medicinal materials.