Real-time safety distance detection and warning for elevator inspection based on adaptive monocular depth estimation

Abstract

During elevator inspection, workers are prone to collisions with hazards in narrow elevator machine rooms. Because of the numerous elevator machine rooms with diverse scenes in cities, it is difficult to effectively monitor the distance between workers and hazards. We propose a real-time safety distance detection and warning system consisting of four parts: object detection, depth estimation, safety distance detection, and safety warning. By introducing two adaptive error correction terms, the proposed adaptive monocular depth estimation algorithm effectively reduces the distortion of object pixel size with changes in depth and shooting angle. Then the calculation formula has been improved to accurately measure the distance between objects. The system has been successfully applied to safety supervision at the elevator inspection site in Nanchang City, Jiangxi Province, China. The results, with an average relative error of 3.34% and 84 frames detected per second, show the system exhibits excellent accuracy and real-time performance.