A Study on the Development of an Automatic Counter Based on Artificial Intelligence Object Recognition

This paper proposes an intelligent automatic counting system leveraging a decentralized edge-server architecture to optimize micro-part enumeration in smart manufacturing. Unlike conventional weight-based or static 2D vision systems that suffer from occlusion and environmental sensitivity, the proposed system introduces a physical dispersion mechanism using high-frequency vibration and high-uniformity LED backlighting. This hardware-software synergy ensures robust object separation and maximizes visual contrast for a YOLOv8s-based detection engine. Experimental results demonstrate a counting accuracy of 92.4% and a processing throughput of 12.5 pieces per second, maintaining a stable 30.2 FPS transmission via a WebSocket-based data pipeline. The proposed approach overcomes the computational constraints of embedded devices while providing a scalable, high-speed solution for labor-intensive manufacturing processes.