How to Build an Autonomous Defect Detector with Physical AI

Aarnav Shah describes a fully local physical AI system that uses an overhead webcam, a Roboflow-trained RF-DETR detector, and a Hiwonder MaxArm robot to identify and remove defective wooden blocks autonomously. The workflow detects defects and valid objects, maps detected pixel centers to physical coordinates through a homography calibration, commands the suction-equipped arm over USB serial to pick and discard defective parts, and rescans the workspace to verify each pick. The project repository separates detection, calibration, coordinate mapping, arm control, data capture, configuration, and testing, while deployment requires users to set hardware-specific bounds and recalibrate. A small demonstration dataset of 10 images was annotated with “Defect” and “Good” classes, augmented to 40 frames, and used to train an RF-DETR-large model that reportedly achieved 97% validation mAP and roughly 0.2-second local inference on Apple Silicon, though the author notes that production systems need substantially larger datasets. Automated 16-point calibration produced a reported mean positioning error of 1.8 mm, and practical issues such as camera overexposure, vacuum leaks caused by drilled holes, and unreliable serial movement acknowledgments were addressed through exposure tuning, grip adjustments, and position polling. The detect-map-act-verify architecture can be adapted to applications including manufacturing inspection, electronics, agriculture, lumber, textiles, and pharmaceutical packaging by changing the training data, calibration, end effector, and robot hardware.

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