Performance Analysis of Lightweight Models for Product Object Classification at Edge Devices

엣지 단말에서 상품 객체 분류를 위한 경량 모델 성능 분석

The use of edge device-based object classification technologies is expanding in areas such as smart farming, automated harvesting robots, and distribution automation. As a result, the demand for lightweight deep learning models that can support efficient real-time processing is increasing. This study evaluates the performance of three representative lightweight models-MobileNetV4 (Mobile Network Version 4), SHViT (Single-Head Vision Transformer), and ViM (Vision Mamba)-for classification of agricultural products. Experiments were conducted in both a high-performance computing environment with an NVIDIA RTX 4070 GPU and an edge device environment using the Jetson AGX Orin platform. The models were compared in terms of Top-1 accuracy, MACs (Multiply-Accumulate Operations), FPS (Frames Per Second), and NetScore. The results show that MobileNetV4 achieved the best overall balance of accuracy, efficiency, and processing speed. These findings confirm the feasibility of applying lightweight deep learning models to real-time object classification in resource-constrained edge environments and provide useful insights for future applications in agriculture and industrial automation.