Measurement of Safety Distance for Construction Machinery Operation Based on Binocular Vision and Operational State Perception

Construction machinery has been widely used to upgrade the power industry. Nevertheless, high-voltage and high-current operational conditions necessitate stringent safety distance protocols. Simultaneously, the operational machinery autonomy and concentration of live equipment impose considerable constraints on current distance measurement techniques. To address these challenges, we propose a refined measurement method for the safety distance of construction machinery based on binocular vision and target state perception. First, the Edge-Segformer model is employed to achieve fine-grained detection of construction machinery and other targets, dynamic recognition of operational states, and precise segmentation of operational zones. Next, the IGEV-Stereo model is utilized to obtain high-precision disparity maps, and the mapping relationship from pixel coordinates to 3-D space is established based on the triangulation principle. Finally, through local contour analysis and depth information, the key measurement points for the minimum safety distance (MSD) are located, and the shortest Euclidean distance from a key point to the boundary of the operational zone is calculated, serving as the MSD during machinery operation. The experimental results demonstrate that our method achieves a fine-grained detection accuracy of 92.80%, exhibiting a 3-D reconstruction error of 3.23% and MSD measurement error of 6.8%, validating its effectiveness in various substation scenarios.