CRIIS - Indexed Articles in Journals

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Browsing CRIIS - Indexed Articles in Journals by Author "5761"
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    Deep Learning YOLO-Based Solution for Grape Bunch Detection and Assessment of Biophysical Lesions
    ( 2023) Pinheiro,I ; Moreira,G ; Daniel Queirós Silva ; Magalhães,S ; Valente,A ; Paulo Moura Oliveira ; Mário Cunha ; Filipe Neves Santos ; 5761 ; 7332 ; 8276 ; 5552
    The world wine sector is a multi-billion dollar industry with a wide range of economic activities. Therefore, it becomes crucial to monitor the grapevine because it allows a more accurate estimation of the yield and ensures a high-quality end product. The most common way of monitoring the grapevine is through the leaves (preventive way) since the leaves first manifest biophysical lesions. However, this does not exclude the possibility of biophysical lesions manifesting in the grape berries. Thus, this work presents three pre-trained YOLO models (YOLOv5x6, YOLOv7-E6E, and YOLOR-CSP-X) to detect and classify grape bunches as healthy or damaged by the number of berries with biophysical lesions. Two datasets were created and made publicly available with original images and manual annotations to identify the complexity between detection (bunches) and classification (healthy or damaged) tasks. The datasets use the same 10,010 images with different classes. The Grapevine Bunch Detection Dataset uses the Bunch class, and The Grapevine Bunch Condition Detection Dataset uses the OptimalBunch and DamagedBunch classes. Regarding the three models trained for grape bunches detection, they obtained promising results, highlighting YOLOv7 with 77% of mAP and 94% of the F1-score. In the case of the task of detection and identification of the state of grape bunches, the three models obtained similar results, with YOLOv5 achieving the best ones with an mAP of 72% and an F1-score of 92%.
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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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    PID Posicast Control for Uncertain Oscillatory Systems: A Practical Experiment
    ( 2018) Josenalde Barbosa Oliveira ; Paulo Moura Oliveira ; Tatiana Martins Pinho ; José Boaventura ; 6636 ; 5983 ; 5773 ; 5761