In this study, we propose a deep learning-based forest road recognition technology and integrate it into an autonomous steering and speed control system for a forestry forwarder. We trained a YOLOv8 segmentation model on 640×480 pixel images collected from straight, curved, and occluded sections of forestry roads, achieving a precision of 0.997, a recall of 0.994, an F1 score of 0.995, and an average precision of 0.994. We developed a centerline extraction algorithm that calculates the lateral offset between the vehicle center and the detected road centerline using the segmented mask. This offset is then input into an electronic steering lever control system, which replaces the existing mechanical steering lever to enable rapid response. In field experiments conducted on actual forest roads, the maximum average path tracking error was 0.534 m over three trials, confirming that the vision-based approach can reliably guide the forwarder under various lighting conditions, shadows, and roadside structures. This method is expected to overcome the limitations of GNSS/INS-based autonomous driving systems, as it operates solely with cameras and single-board hardware. The research results suggest that deep learning-based image processing provides a solid foundation for fully autonomous forestry operations and has the potential to improve the productivity and safety of timber transport operations.
저자
Hyeon-Seung Lee [ Forest Technology and Management Research Center, National Institute of Forest Science ]
Gyun-Hyung Kim [ Forest Technology and Management Research Center, National Institute of Forest Science ]
Hong-Sik Ju [ Forest Technology and Management Research Center, National Institute of Forest Science ]
Ho-Seong Mun [ Forest Technology and Management Research Center, National Institute of Forest Science ]
Jae-Heun Oh [ Forest Technology and Management Research Center, National Institute of Forest Science ]
Corresponding Author
