Application of Artificial Intelligence in Particle and Impurity Detection and Removal: A Survey

Shuyuti, Nur Aini Syakimah Ahmad; Salami, Erfan; Dahari, Mahidzal; Arof, Hamzah; Ramiah, Harikrishnan

doi:10.1109/access.2024.3351858

IEEE Access

2024

DOI: 10.1109/access.2024.3351858

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Application of Artificial Intelligence in Particle and Impurity Detection and Removal: A Survey

Nur Aini Syakimah Ahmad Shuyuti,

Erfan Salami,

Mahidzal Dahari

et al.

Abstract: With the rapid development of artificial intelligence (AI), especially in machine learning and deep learning technologies, the particle and impurity detection and removal processes employed many industries have been improved. Particles and impurities at any size, shape and at any condition can be detected using the advanced technology in both areas. This paper presents a comprehensive overview of research papers that discuss the application of AI techniques for the detection and removal of particles and impuri… Show more

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2024

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Cited by 2 publications

References 77 publications

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Deep Learning to Authenticate Traditional Handloom Textile

Das,

Deka,

Medhi

et al. 2024

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Handloom textile products play an essential role in both the financial and cultural landscape of natives, necessitating accurate and efficient methods for authenticating against replicated powerloom textiles for the protection of heritage and indigenous weavers’ economic viability. This paper presents a new approach to the automated identification of handloom textiles leveraging a deep metric learning technique. A labeled handloom textile dataset of 25,166 images was created by collecting handloom textile samples of six unique types, working with indigenous weavers in Assam, Northeast India. The proposed method achieved remarkable success by acquiring biased feature representations that facilitate the effective separation of different fiber types in a learned feature space. Through extensive experimentation and comparison with baseline models, our approach demonstrated superior efficiency in classifying handloom textiles with an accuracy of 97.8%. Our approach not only contributes to the preservation and promotion of traditional textile craftsmanship in the region but also highlights its significance.

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Deep Learning to Authenticate Traditional Handloom Textile

Das,

Deka,

Medhi

et al. 2024

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A Computer Vision Model for Seaweed Foreign Object Detection Using Deep Learning

Zhang,

Alhendi,

Hamid

et al. 2024

Sustainability

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Seaweed foreign object detection has become crucial for food consumption and industrial use. This process not only can prevent potential health issues, but also maintain the overall marketability of seaweed production in the food industry. Traditional methods of inspecting seaweed foreign objects heavily rely on human judgment, which deals with large volumes with diverse impurities and can be inconsistent and inefficient. An automation system for real-time seaweed foreign object detection in the inspection process should be adopted. However, automated seaweed foreign object detection has several challenges due to its dependency on visual input inspection, such as an uneven surface and undistinguishable impurities. In fact, limited access to advanced technologies and high-cost equipment would also influence visual input acquisition, thereby hindering the advancement of seaweed foreign object detection in this field. Therefore, we introduce a computer vision model utilizing a deep learning-based algorithm to detect seaweed impurities and classify the samples into ‘clean’ and ‘unclean’ categories. In this study, we managed to identify six types of seaweed impurities including sand sticks, shells, discolored seaweed, grass, worm shells, and mixed impurities. We collected 1204 images and our model’s performance was thoroughly evaluated based on comparisons with three pre-trained models, i.e., Yolov8, ResNet, and MobileNet. Our experiment shows that Yolov8 outperforms the other two models with an accuracy of 98.86%. This study also included the development of an Android application to validate the deep learning engine to ensure its optimal performance. Based on our experiments, the mobile application managed to classify 50 pieces of seaweed samples within 0.2 s each, showcasing its potential use in large-scale production lines and factories. This research demonstrates the impact of Artificial Intelligence on food safety by offering a scalable and efficient solution that can be deployed in other food production processes facing similar challenges. Our approach paves the way for broader industry adoption and advancements in automated foreign object detection systems by optimizing detection accuracy and speed.

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Application of Artificial Intelligence in Particle and Impurity Detection and Removal: A Survey

Cited by 2 publications

References 77 publications

Deep Learning to Authenticate Traditional Handloom Textile

Deep Learning to Authenticate Traditional Handloom Textile

A Computer Vision Model for Seaweed Foreign Object Detection Using Deep Learning

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