Deep-Learning-Based Automatic Mineral Grain Segmentation and Recognition

Latif, Ghazanfar; Bouchard, Kévin; Maître, Julien; Back, Arnaud; Bèdard, L. Paul

doi:10.3390/min12040455

Cited by 35 publications

(19 citation statements)

References 46 publications

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“…ResNet-50 is a standard and widely used model in computer vision applications and has demonstrated its effectiveness in a number of studies, − such as AquaVision . Introduced in 2015 as part of the ResNet family of models, its popularity can be attributed to its ability to handle complex data sets.…”

Section: Resultsmentioning

confidence: 99%

Freshwater Microscopic Algae Detection Based on Deep Neural Network with GAN-Based Augmentation for Imbalanced Algal Data

Fung,

Chan,

et al. 2023

ACS EST Water

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Identifying and quantifying algal genera in images are crucial for understanding their ecological impact. Algal data are often imbalanced, limiting detection model accuracy. This paper presents a novel data augmentation method using StyleGAN2-ADA to enhance algal image instance segmentation. StyleGAN2-ADA generates artificial single-algal images to address data scarcity and imbalance. We train a Cascaded Mask R-CNN with Swin Transformer on a combined data set of real and artificial multigenera algal images and evaluate performance using the COCO mAP metric. The approach improves bounding box detection performance by 17.9% on all genera and 32.1% on rare genera compared with the baseline model. Additionally, 50% more artificial data yield significant enhancements without excessive artificial data use. The GAN-based augmentation technique shows a performance improvement in both Swin-Tiny and ResNet-50 backbone models, suggesting adaptability for various machine learning models. The increased mAP leads to the accurate identification of harmful algae genera, allowing for better prevention and mitigation. This method offers a superior data augmentation solution for accurate algal instance segmentation and can benefit applications challenged by imbalanced and scarce data.

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Section: Resultsmentioning

confidence: 99%

Freshwater Microscopic Algae Detection Based on Deep Neural Network with GAN-Based Augmentation for Imbalanced Algal Data

Fung,

Chan,

et al. 2023

ACS EST Water

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“…For example, Fu et al [ 104 ] used ResNet‐34 to establish a general quantitative description of minerals and predict their qualities via surface texture feature learning. Latif [ 105 ] adopted two residual network architectures (ResNet 1 and ResNet 2) that contained 47 layers and achieved more than 90% validation accuracy.…”

Section: Single‐modal Recognition Of Mineral/rock Datamentioning

confidence: 99%

Application of Image Sensing System in Mineral/Rock Identification: Sensing Mode and Information Process

Zhang,

Yang,

Sun

et al. 2023

Advanced Intelligent Systems

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Artificial intelligence promotes the development of mineral/rock automatic recognition technology to reduce labor costs and reliance on personal experience. With instrument digitization, image recognition has played an increasingly important role. This review focuses on the image sensing system applied to mineral/rock identification and redivides this system into a sensing module, an imaging module, and an information processing module. The sensing and imaging mechanisms are summarized via technologies of sensing, imaging, and display. The information processing is classified into single‐modal or multimodal recognition. This review analyzes the limitations of single‐modal recognition and discusses the advantages and significance of multimodal recognition. The challenges of current mineral/rock identification are concluded, such as public database, multimodal learning, and portable sensing system. The interdisciplinary sensing technologies are discussed, and application feasibility is explored. This review aims to provide a reference for the research community in automatic mineral/rock identification and sensing system optimization.

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“…Convolutional Neural Networks (CNN or ConvNet), a class of deep neural networks specialized in image recognition, have developed tremendously in recent years in various fields, including agriculture. CNN uses multiple blocks of convolutional layers, pooling layers, and fully connected layers to create conceptual spatial-temporal hierarchies of features using backpropagation in an adaptive and self-optimizing manner [42]. The main idea of CNN is to build a deeper network with a much smaller number of parameters.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model

Latif

Abdelhamid

Mallouhy

et al. 2022

Plants

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114

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Rice is considered one the most important plants globally because it is a source of food for over half the world’s population. Like other plants, rice is susceptible to diseases that may affect the quantity and quality of produce. It sometimes results in anywhere between 20–40% crop loss production. Early detection of these diseases can positively affect the harvest, and thus farmers would have to be knowledgeable about the various disease and how to identify them visually. Even then, it is an impossible task for farmers to survey the vast farmlands on a daily basis. Even if this is possible, it becomes a costly task that will, in turn, increases the price of rice for consumers. Machine learning algorithms fitted to drone technology combined with the Internet of Things (IoT) can offer a solution to this problem. In this paper, we propose a Deep Convolutional Neural Network (DCNN) transfer learning-based approach for the accurate detection and classification of rice leaf disease. The modified proposed approach includes a modified VGG19-based transfer learning method. The proposed modified system can accurately detect and diagnose six distinct classes: healthy, narrow brown spot, leaf scald, leaf blast, brown spot, and bacterial leaf blight. The highest average accuracy is 96.08% using the non-normalized augmented dataset. The corresponding precision, recall, specificity, and F1-score were 0.9620, 0.9617, 0.9921, and 0.9616, respectively. The proposed modified approach achieved significantly better results compared with similar approaches using the same dataset or similar-size datasets reported in the extant literature.

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Deep-Learning-Based Automatic Mineral Grain Segmentation and Recognition

Cited by 35 publications

References 46 publications

Freshwater Microscopic Algae Detection Based on Deep Neural Network with GAN-Based Augmentation for Imbalanced Algal Data

Freshwater Microscopic Algae Detection Based on Deep Neural Network with GAN-Based Augmentation for Imbalanced Algal Data

Application of Image Sensing System in Mineral/Rock Identification: Sensing Mode and Information Process

Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model

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