CRIIS - Indexed Articles in Journals

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Browsing CRIIS - Indexed Articles in Journals by Author "5843"
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    Edge AI-Based Tree Trunk Detection for Forestry Monitoring Robotics
    ( 2022) Daniel Queirós Silva ; Filipe Neves Santos ; Vitor Manuel Filipe ; Armando Sousa ; Paulo Moura Oliveira ; 5152 ; 5761 ; 8276 ; 5843 ; 5552
    Object identification, such as tree trunk detection, is fundamental for forest robotics. Intelligent vision systems are of paramount importance in order to improve robotic perception, thus enhancing the autonomy of forest robots. To that purpose, this paper presents three contributions: an open dataset of 5325 annotated forest images; a tree trunk detection Edge AI benchmark between 13 deep learning models evaluated on four edge-devices (CPU, TPU, GPU and VPU); and a tree trunk mapping experiment using an OAK-D as a sensing device. The results showed that YOLOR was the most reliable trunk detector, achieving a maximum F1 score around 90% while maintaining high scores for different confidence levels; in terms of inference time, YOLOv4 Tiny was the fastest model, attaining 1.93 ms on the GPU. YOLOv7 Tiny presented the best trade-off between detection accuracy and speed, with average inference times under 4 ms on the GPU considering different input resolutions and at the same time achieving an F1 score similar to YOLOR. This work will enable the development of advanced artificial vision systems for robotics in forestry monitoring operations.
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    SCARA Self Posture Recognition Using a Monocular Camera
    ( 2022) Vítor Tinoco ; Manuel Santos Silva ; Filipe Neves Santos ; Morais,R ; Vitor Manuel Filipe ; 5655 ; 8387 ; 5843 ; 5552
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    Unimodal and Multimodal Perception for Forest Management: Review and Dataset
    ( 2021) Daniel Queirós Silva ; Filipe Neves Santos ; Armando Sousa ; Vitor Manuel Filipe ; José Boaventura ; 5152 ; 5552 ; 5773 ; 8276 ; 5843
    Robotics navigation and perception for forest management are challenging due to the existence of many obstacles to detect and avoid and the sharp illumination changes. Advanced perception systems are needed because they can enable the development of robotic and machinery solutions to accomplish a smarter, more precise, and sustainable forestry. This article presents a state-of-the-art review about unimodal and multimodal perception in forests, detailing the current developed work about perception using a single type of sensors (unimodal) and by combining data from different kinds of sensors (multimodal). This work also makes a comparison between existing perception datasets in the literature and presents a new multimodal dataset, composed by images and laser scanning data, as a contribution for this research field. Lastly, a critical analysis of the works collected is conducted by identifying strengths and research trends in this domain.
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    Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics
    ( 2021) Daniel Queirós Silva ; Filipe Neves Santos ; Armando Sousa ; Vitor Manuel Filipe ; 5152 ; 5552 ; 8276 ; 5843
    Mobile robotics in forests is currently a hugely important topic due to the recurring appearance of forest wildfires. Thus, in-site management of forest inventory and biomass is required. To tackle this issue, this work presents a study on detection at the ground level of forest tree trunks in visible and thermal images using deep learning-based object detection methods. For this purpose, a forestry dataset composed of 2895 images was built and made publicly available. Using this dataset, five models were trained and benchmarked to detect the tree trunks. The selected models were SSD MobileNetV2, SSD Inception-v2, SSD ResNet50, SSDLite MobileDet and YOLOv4 Tiny. Promising results were obtained; for instance, YOLOv4 Tiny was the best model that achieved the highest AP (90%) and F1 score (89%). The inference time was also evaluated, for these models, on CPU and GPU. The results showed that YOLOv4 Tiny was the fastest detector running on GPU (8 ms). This work will enhance the development of vision perception systems for smarter forestry robots.