Defect Detection in Synthetic Fibre Ropes using Detectron2 Framework

Rani, Anju; Ortiz-Arroyo, Daniel; Durdevic, Petar

doi:10.1016/j.apor.2024.104109

Applied Ocean Research

2024

DOI: 10.1016/j.apor.2024.104109

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Defect Detection in Synthetic Fibre Ropes using Detectron2 Framework

Anju Rani,

Daniel Ortiz-Arroyo,

Petar Durdevic

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2024

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Cited by 2 publications

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A survey of vision-based condition monitoring methods using deep learning: A synthetic fiber rope perspective

Rani,

Ortiz-Arroyo,

Durdevic

2024

Engineering Applications of Artificial Intelligence

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A survey of vision-based condition monitoring methods using deep learning: A synthetic fiber rope perspective

Rani,

Ortiz-Arroyo,

Durdevic

2024

Engineering Applications of Artificial Intelligence

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Detection and Classification of Agave angustifolia Haw Using Deep Learning Models

Matadamas,

Zamora,

Aquino-Bolaños

2024

Agriculture

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In Oaxaca, Mexico, there are more than 30 species of the Agave genus, and its cultivation is of great economic and social importance. The incidence of pests, diseases, and environmental stress cause significant losses to the crop. The identification of damage through non-invasive tools based on visual information is important for reducing economic losses. The objective of this study was to evaluate and compare five deep learning models: YOLO versions 7, 7-tiny, and 8, and two from the Detectron2 library, Faster-RCNN and RetinaNet, for the detection and classification of Agave angustifolia plants in digital images. In the town of Santiago Matatlán, Oaxaca, 333 images were taken in an open-air plantation, and 1317 plants were labeled into five classes: sick, yellow, healthy, small, and spotted. Models were trained with a 70% random partition, validated with 10%, and tested with the remaining 20%. The results obtained from the models indicate that YOLOv7 is the best-performing model, in terms of the test set, with a mAP of 0.616, outperforming YOLOv7-tiny and YOLOv8, both with a mAP of 0.606 on the same set; demonstrating that artificial intelligence for the detection and classification of Agave angustifolia plants under planting conditions is feasible using digital images.

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Defect Detection in Synthetic Fibre Ropes using Detectron2 Framework

Cited by 2 publications

References 20 publications

A survey of vision-based condition monitoring methods using deep learning: A synthetic fiber rope perspective

A survey of vision-based condition monitoring methods using deep learning: A synthetic fiber rope perspective

Detection and Classification of Agave angustifolia Haw Using Deep Learning Models

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