In response to the current issues of poor real-time performance, high computational costs, and excessive memory usage of object detection algorithms based on deep convolutional neural networks in embedded devices, a method for improving deep convolutional neural networks based on model compression and knowledge distillation is proposed. Firstly, data augmentation is employed in the preprocessing stage to increase the diversity of training samples, thereby improving the model’s robustness and generalization capability. The K-means++ clustering algorithm generates candidate bounding boxes, adapting to defects of different sizes and selecting finer features earlier. Secondly, the cross stage partial (CSP) Darknet53 network and spatial pyramid pooling (SPP) module extract features from the input raw images, enhancing the accuracy of defect location detection and recognition in YOLO. Finally, the concept of model compression is integrated, utilizing scaling factors in the batch normalization (BN) layer, and introducing sparse factors to perform sparse training on the network. Channel pruning and layer pruning are applied to the sparse model, and post-processing methods using knowledge distillation are used to effectively reduce the model size and forward inference time while maintaining model accuracy. The improved model size decreases from 244 M to 4.19 M, the detection speed increases from 32.8 f/s to 68 f/s, and mAP reaches 97.41. Experimental results demonstrate that this method is conducive to deploying network models on embedded devices with limited GPU computing and storage resources. It can be applied in distributed service architectures for edge computing, providing new technological references for deploying deep learning models in the industrial sector.

Distinguishing Alzheimer’s Disease (AD) from Mild Cognitive Impairment (MCI) is challenging due to their subtle morphological similarities in MRI, yet distinct therapeutic strategies are required. To assist junior clinicians with limited diagnostic experience, this paper proposes Vi-ADiM, a Vision Transformer framework designed for the early differentiation of AD and MCI. Leveraging cross-domain feature adaptation and task-specific data augmentation, the model ensures rapid convergence and robust generalization even in data-limited regimes. By optimizing a two-stage encoding module, Vi-ADiM efficiently extracts both global and local MRI features. Furthermore, by integrating SHAP and Grad-CAM++, the framework offers multi-granular interpretability of pathological regions, providing intuitive visual evidence for clinical decision-making. Experimental results demonstrate that Vi-ADiM outperforms the standard ViT-Base/16, improving accuracy, precision, recall, and F1 score by 0.444%, 0.486%, 0.476%, and 0.482%, respectively, while reducing standard deviations by approximately 0.06–0.29%. Notably, the model achieves these gains with a 48.96% reduction in parameters and a 49.65% decrease in computational cost (FLOPs), offering a reliable, efficient, and interpretable solution for computer-aided diagnosis.