2025 Volume 6 Issue 1 Pages 245-257
Location information of workers at construction sites is widely used to improve safety and productivity. Various devices, such as cameras, GNSS, and BLE beacons are used to obtain this information. However, cameras are susceptible to illuminance variations, GNSS is difficult to use indoors and in underground spaces, and BLE beacons require a substantial number of receivers for comprehensive coverage of expansive sites. These limitations present practical challenges.
To address these challenges, this study proposes a novel method for detecting workers using low-cost LiDAR. Unlike other technologies, LiDAR is unaffected by lighting conditions and performs well in indoor, underground, and expansive environments. The proposed approach combines deep learning with motion detection techniques to enhance accuracy and reliability. Verification experiments achieved an F-measure detection accuracy of approximately 0.9, demonstrating the method’s potential for precise workers localization at construction sites.
