Rail gage-based risk detection Using iPhone 12 pro

Abstract

Federal Railroad Administration strictly regulates the inspection frequency of all track classes to ensure timely identification of rail defects including irregular gage which is a devastating rail geometry defect. Conventional rail inspection methods are both costly and labor-intensive, whereas existing novel technologies can be expensive and mostly focus on a specific inspection area, e.g. vertical alignment. iPhone 12 Pro was introduced to the public recently with a low-cost, low-resolution light detection and ranging (LiDAR) sensor that is purposed for better photography and virtual reality. Thanks to its portability and computational capacity, iPhone 12 Pro can potentially be used as a portable solution for irregular gage inspection, whose capacity and feasibility are unknown. This study first investigated the capability of the iPhone 12 Pro in calculating unloaded rail gages by its embedded LiDAR sensor. The results showed that uncalibrated raw gage values measured by the iPhone 12 Pro LiDAR sensor were systematically lower than the ground-truth values. The proposed method in this study then introduced logistic regression to calibrate the measured values through balancing the prediction performance and the efficiency, followed by validations using a Gaussian process classifier. The results show that the proposed method correctly identified all 39 high-risk locations with 227 false alarmed locations. The proposed method with the iPhone 12 Pro LiDAR sensor could potentially narrow down the possible “high-risk” gage sections and may result in a significant reduction in the field inspection workload by 48%.