YOLOv8 fine-tuned for real-time industrial defect detection on edge

Researchers have developed Industrial-YOLO, a framework using a fine-tuned YOLOv8 model for real-time defect detection on edge hardware. This system was benchmarked on the NEU surface defect database and MVTec AD, with added automotive manufacturing extensions. The framework achieves over 120 FPS on an NVIDIA Jetson Orin platform with a 98.5% mAP, demonstrating robust, zero-latency performance suitable for automated optical inspection systems. AI

影响 Enables high-speed, low-latency defect detection in manufacturing environments, potentially improving quality control and reducing costs.

排序理由 The cluster contains an academic paper detailing a new framework and benchmark results for a specific application. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

• Emmanuel Somtochukwu Ezeji
• Industrial-YOLO
• MVTec AD
• NEU surface defect database
• NVIDIA Jetson Orin
• YOLOv8

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