Refined Safety Distance Measurement of Heavy Construction Machinery Near Electricity Operation Based on Feature‐Homogenised Spatial Target Analysis

Abstract

Heavy construction machinery is essential for power infrastructure construction and maintenance, making safety distance measurement crucial for ensuring operational safety. Existing methods, which rely on target space mapping, face challenges such as error accumulation and limited accuracy. Motivated by this, this paper proposes a refined measurement method using high‐precision, anti‐interference LiDAR as the 3D data source. The method employs multi‐layer set abstraction for 3D feature extraction and introduces instance‐aware downsampling to homogenise point cloud features across targets of varying distances and sizes. Subsequently, an instance centroid prediction module aggregates contextual information to estimate precise spatial positions. Ultimately, by integrating optimal representative point selection with a distance calculation mechanism, the method achieves accurate minimum safety distance measurement for near‐electricity operations. Experimental results demonstrate superior accuracy in target location identification and distance measurement in complex scenes, outperforming existing methods. This approach provides theoretical and practical support for risk prevention in near‐electricity operations of heavy construction machinery.