To tackle wildfires and improve forest biomass management, cost effective and reliable mowing and pruning robots are required. However, the development of visual perception systems for forestry robotics needs to be researched and explored to achieve safe solutions. This paper presents two main contributions: an annotated dataset and a benchmark between edge-computing hardware and deep learning models. The dataset is composed by nearly 5,400 annotated images. This dataset enabled to train nine object detectors: four SSD MobileNets, one EfficientDet, three YOLO-based detectors and YOLOR. These detectors were deployed and tested on three edge-computing hardware (TPU, CPU and GPU), and evaluated in terms of detection precision and inference time. The results showed that YOLOR was the best trunk detector achieving nearly 90% F1 score and an inference average time of 13.7 ms on GPU. This work will favour the development of advanced vision perception systems for robotics in forestry operations.


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    Title :

    Tree Trunks Cross-Platform Detection Using Deep Learning Strategies for Forestry Operations


    Additional title:

    Lect. Notes in Networks, Syst.



    Conference:

    Iberian Robotics conference ; 2022 ; Zaragoza, Spain November 23, 2022 - November 25, 2022



    Publication date :

    2022-11-19


    Size :

    13 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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