Edge ai detects conveyor belt defects in real time on raspberry pi
Real-Time Conveyor Belt Defect Detection via Edge TPU-Accelerated YOLOv11 on Raspberry Pi 5
Computer Vision and Pattern Recognition
Summary
Conveyor belts in heavy industries like cement factories can break if their damages go unnoticed, causing big problems. The authors developed a smart camera system using a small, low-power computer with AI to spot four types of belt defects as they happen. Their AI model runs fast and accurately on tiny hardware, even in dusty factory conditions. This helps catch issues early to avoid costly repairs and dangerous accidents.
What this means in practice
- •For industrial maintenance teams: Deploy low-power AI systems at factory sites to monitor conveyor belts for defects and prevent unplanned downtime.
- •For field engineers in heavy industry: Use embedded edge AI to perform onsite visual inspections under harsh environmental conditions without relying on cloud or bulky hardware.
Authors
Muhammad Nur Bijak Bestari, Mulki Indana Zulfa
Abstract
Conveyor belts are vital arteries in continuous bulk material handling, particularly in heavy process industries such as cement manufacturing. Undetected structural anomalies (longitudinal tears, holes, degraded patch repairs, and impact punctures) cause catastrophic belt snapping, costly downtime, and severe safety hazards. While deep learning offers high accuracy, existing models rely on power-hungry workstation GPUs, hindering practical deployment at remote industrial edge points. In this paper, we propose, implement, and validate an end-to-end, low-power edge AI vision system for real-time conveyor belt defect detection deployed on a Raspberry Pi 5 accelerated by a Google Coral Edge TPU coprocessor. The system leverages an optimized YOLOv11 nano (YOLOv11n) architecture trained on a multi-source industrial corpus exceeding 3,000 augmented samples from an operational cement plant in West Java, open-source datasets, and automated captures. The model undergoes post-training full integer quantization (INT8) compiled for systolic tensor execution. Experimental results demonstrate robust detection capability, achieving precision of 0.742, recall of 0.730, and mAP@0.5 of 0.764 across four defect classes. Deployed on-site in an IP-rated enclosure interfaced with an RTSP PoE industrial camera, the system achieves stable throughput of 30-40 FPS at 320x320 resolution with an end-to-end latency of 70-90 ms. Operational profiling under harsh clinker dust confirms thermal equilibrium at 55 +/- 5 deg C on the host CPU and ~65 deg C on the Edge TPU with zero frame loss. This demonstrates that edge AI bridges the gap between high-accuracy computer vision and cost-effective predictive maintenance in continuous manufacturing.