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Article

Implementation of a System for Real-Time Detection and Localization of Terrain Objects on Harvested Forest Land

Department of Engineering Sciences and Mathematics, Luleå University of Technology, 97187 Luleå, Sweden
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Author to whom correspondence should be addressed.
Academic Editors: Timothy A. Martin and Timo Saksa
Forests 2021, 12(9), 1142; https://doi.org/10.3390/f12091142
Received: 24 May 2021 / Revised: 16 August 2021 / Accepted: 20 August 2021 / Published: 24 August 2021
(This article belongs to the Special Issue Digital Transformation and Management in Forest Operations)
Research highlights: An automatic localization system for ground obstacles on harvested forest land based on existing mature hardware and software architecture has been successfully implemented. In the tested area, 98% of objects were successfully detected and could on average be positioned within 0.33 m from their true position in the full range 1–10 m from the camera sensor. Background and objectives: Forestry operations in forest environments are full of challenges; detection and localization of objects in complex forest terrains often require a lot of patience and energy from operators. Successful automatic real-time detection and localization of terrain objects not only can reduce the difficulty for operators but are essential for the automation of harvesting and logging tasks. We intend to implement a system prototype that can automatically locate ground obstacles on harvested forest land based on accessible hardware and common software infrastructure. Materials and Methods: An automatic object detection and localization system based on stereo camera sensing is described and evaluated in this paper. This demonstrated system detects and locates objects of interest automatically utilizing the YOLO (You Only Look Once) object detection algorithm and derivation of object positions in 3D space. System performance is evaluated by comparing the automatic detection results of the tests to manual labeling and positioning results. Results: Results show high reliability of the system for automatic detection and location of stumps and large stones and shows good potential for practical application. Overall, object detection on test tracks was 98% successful, and positional location errors were on average 0.33 m in the full range from 1–10 m from the camera sensor. Conclusions: The results indicate that object detection and localization can be used for better operator assessment of surroundings, as well as input to control machines and equipment for object avoidance or targeting. View Full-Text
Keywords: automation; object identification; obstacle detection; terrain obstacle automation; object identification; obstacle detection; terrain obstacle
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MDPI and ACS Style

Li, S.; Lideskog, H. Implementation of a System for Real-Time Detection and Localization of Terrain Objects on Harvested Forest Land. Forests 2021, 12, 1142. https://doi.org/10.3390/f12091142

AMA Style

Li S, Lideskog H. Implementation of a System for Real-Time Detection and Localization of Terrain Objects on Harvested Forest Land. Forests. 2021; 12(9):1142. https://doi.org/10.3390/f12091142

Chicago/Turabian Style

Li, Songyu, and Håkan Lideskog. 2021. "Implementation of a System for Real-Time Detection and Localization of Terrain Objects on Harvested Forest Land" Forests 12, no. 9: 1142. https://doi.org/10.3390/f12091142

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