Human intrusion detection based on LiDAR-camera fusion
TL;DR
To ensure the safety of personnel working in coal-fired power generation corridors, a out-of-bounds detection method based on LiDAR and camera fusion was proposed and a out-of-bounds warning and alarm mechanism based on a logical decision tree was designed.
Abstract
Energy and power systems are fundamental to modern society, particularly to the development of the AI industry. Coal-fired power plants operate in complex and hazardous environments that pose substantial risks to personnel safety. To ensure the safety of personnel working in coal-fired power generation corridors, a out-of-bounds detection method based on LiDAR and camera fusion was proposed. To address the issue of sensor attitude offset, IMU data was used to horizontally correct the point cloud. Occlusion detection and clustering algorithms were then combined to extract dynamic bounding boxes for personnel. To accurately identify the spatial relationship between key human body parts and hazardous areas, a 3D joint localization algorithm based on image-point cloud fusion was constructed. Furthermore, a out-of-bounds warning and alarm mechanism based on a logical decision tree was designed. This mechanism analyzes behavioral intent by combining personnel orientation and joint distribution, and uses a ray method to determine whether a joint has entered a hazardous area. Field measurements demonstrated that the system achieved an 80% warning accuracy rate, an 87.3% out-of-bounds alarm accuracy rate, and an average response delay of 89.25 ms; meanwhile, the single-frame error of the 3D joint localization algorithm was controlled within 0.073 m, validating the effectiveness and practicality of this method.