Perspective Transform-based Depth Estimation of Monocular Camera for Electrocution Threat Determination of Construction Machinery

Abstract

This paper discusses the application of perspective transform-based monocular camera depth estimation method in monitoring the safety distance between construction machinery and power lines. The traditional distance measurement relies on manual operation, which is inefficient and not precise enough, while the monocular depth estimation method based on deep learning can improve the accuracy, but faces high equipment cost and relies on a large amount of training data. In contrast, the depth estimation method based on perspective transformation proposed in this paper utilizes mathematical and physical principles to establish a mathematical model between the pixel distances of an image and the actual scene distances through perspective projection theory, which simplifies the hardware requirements and reduces the data dependence and improves the computational efficiency and applicability. Through experimental validation under different construction scenes and exposure conditions, the method demonstrates high accuracy and robustness, proving its practicality in construction safety management. This research provides a new technical direction and practical application possibility in the field of intelligent monitoring and 3D reconstruction.