Fast Rock Detection in Visually Contaminated Mining Environments Using Machine Learning and Deep Learning Techniques

Advances in machine learning algorithms have allowed object detection and classification to become booming areas. The detection of objects, such as rocks, in mining operations is affected by fog, snow, suspended particles, and high lighting. These environmental conditions can stop the development of...

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Bibliographic Details
Published in:Applied sciences 2024-01, Vol.14 (2), p.731
Main Authors: Rodriguez-Guillen, Reinier, Kern, John, Urrea, Claudio
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Deep learning
deep learning techniques
Design
Embedded systems
Machine learning
machine learning algorithms
Methods
Mines and mineral resources
mining environments
Neural networks
rock detection
Sensors
Support vector machines
visual contamination
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Summary:Advances in machine learning algorithms have allowed object detection and classification to become booming areas. The detection of objects, such as rocks, in mining operations is affected by fog, snow, suspended particles, and high lighting. These environmental conditions can stop the development of mining work, which entails a considerable increase in operating costs. It is vital to select a machine learning algorithm that is accurate, fast, and contributes to lower operational costs because of the aforementioned environmental situations. In this study, the Viola-Jones algorithm, Aggregate Channel Features (ACF), Faster Regions with Convolutional Neural Networks (Faster R-CNN), Single-Shot Detector (SSD), and You Only Look Once (YOLO) version 4 were analyzed, considering the precision metrics, recall, AP50, and average detection time. In our preliminary tests, we have observed that the differences between YOLO v4 and the latest versions are not substantial for the specific problem of rock detection addressed in our article. Therefore, YOLO v4 is an appropriate and representative choice for evaluating the effectiveness of existing methods in our study. The YOLO v4 algorithm performed the best overall, whereas the SSD algorithm performed the fastest. The results indicate that the YOLO v4 algorithm is a promising candidate for detecting rocks with visual contamination in mining operations.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14020731