To assist visually impaired individuals in navigation, an AI-based machine vision system for collecting tactile paving information has been designed to enable intelligent data acquisition. The system identifies standard tactile paving through image edge detection and morphological constraints, enabling wheeled robots to autonomously traverse tactile paths. It employs the YOLOv8 object detection model paired with Huawei Ascend AI processors to detect anomalies in tactile paving, transmitting detection results to a host computer via Wi-Fi. During testing, simulated tactile paving tiles measuring 30cm × 30cm were laid out. Multiple detection runs were conducted with damaged tactile paving, missing sections, and both movable and immovable obstacles placed at various positions. Testing confirmed the system's capability to collect tactile paving data within defined scenarios, identify anomaly types and locations, with an average detection accuracy of 95%. The average absolute error in anomaly location pinpointing was less than 9.12 cm relative to actual positions. This system can assist municipal authorities in understanding tactile paving conditions and support safe travel for visually impaired individuals.