earticle

영어

Timber harvesting in forested areas involves the simultaneous operation of heavy forest machines and manual workers, creating a high risk of collision-related accidents. To address these safety concerns, this study developed a deep learning-based human detection sensor system designed for installation on forest forwarders. The system integrates a Raspberry Pi 4 with a Pi-camera running a MobileNetV2-based detection model, optimized for real-time inference under low-power embedded conditions. In addition, ultrasonic sensors were incorporated to measure distances to detected person, enabling accurate localization around the machine. Model training utilized a filtered COCO dataset and achieved optimal performance through augmentation strategies, with the customized M3 configuration and our own test dataset reaching a mean average precision (mAP) of 0.71, precision of 0.95, and recall of 0.99. Experimental evaluations confirmed that the system successfully detected person across various postures, positions, and environmental conditions, with localization errors maintained within acceptable limits. Outdoor tests further demonstrated robust performance even under partial occlusion, although occasional false negatives in complex or low-light scenarios highlighted the need for dataset expansion and sensor fusion. The developed system transmits integrated detection and localization data via CAN bus, confirming its feasibility for deployment in actual forest forwarders. These findings suggest that the proposed sensor system offers a promising solution for enhancing worker safety in mechanized forestry operations and provides a foundation for future smart and autonomous forest machines.