Earticle

초록

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.

목차

Abstract
Introduction
Materials and Methods
Deep learning model to recognize person
Image data set
Deep learning training
Model performance metrics
Test dataset for model evaluation
Extraction of person object’s direction
Ultrasonic distance measurement
Integrated human-detection system
Performance under various positions, postures, and environmental conditions
Results and Discussion
Performance evaluation of deep learning model
Person object detection results
Human identification and localization under various postures and positions
Conclusion
Acknowledgements
References

키워드

저자

• Gyu-Han Son [ Department of Biosystems Engineering, Kangwon National University, Chuncheon 24341, Republic of Korea ]
• Beom-Soo Shin [ Department of Biosystems Engineering, Kangwon National University, Chuncheon 24341, Republic of Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  Journal of Forest and Environmental Science [산림과학연구]

• 간기

  계간

• pISSN

  2288-9744

• eISSN

  2288-9752

• 수록기간

  1981~2025

• 등재여부

  KCI 등재

• 십진분류

  KDC 526 DDC 634