Real-Time Plastic Surface Defect Detection Using Deep Learning

Roslan, Muhammad Izzat; İbrahim, Zarina Bibi; Aziz, Zalilah Abd

doi:10.1109/iscaie54458.2022.9794475

Cited by 10 publications

(4 citation statements)

References 15 publications

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“…Nevertheless, the two decisions depend on the trade-off between precision and complexity and the ease of data acquisition. Moreover, ML algorithms can potentially facilitate quality control by predicting the properties and performance of the resulting bioplastic products, thereby enhancing their reliability and market acceptance [132].…”

Section: Potential Of Machine Learning In the Production Of Algal Bio...mentioning

confidence: 99%

Towards a Sustainable Circular Economy: Algae‐Based Bioplastics and the Role of Internet‐of‐Things and Machine Learning

Bin Abu Sofian,

Lim,

Manickam

et al. 2023

ChemBioEng Reviews

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The growing potential of sustainable materials such as polyhydroxyalkanoates (PHAs), polylactic acid (PLA), alginate, carrageenan, and ulvan for bioplastics production presents an opportunity to promote a sustainable circular economy. This review investigates their properties, applications, and challenges. Bioplastics derived from algae offer an environmentally friendly alternative to petroleum‐based plastics, a shift of paramount importance to society due to the escalating environmental concerns associated with traditional plastics. The role of the internet‐of‐things (IoT) and machine learning in refining these bioplastics' production and development processes is emphasized. IoT monitors cultivation conditions, data collection, and process control for more sustainable production. Machine learning can enhance algae cultivation, increasing the supply of raw materials for algal bioplastics and improving their efficiency and output. The study results indicate the promise of algae‐based bioplastics, IoT, and machine learning in fostering a more environmentally sustainable future. By harnessing these advanced technologies, optimization of bioplastic production is possible, potentially revolutionizing the materials industry and addressing existing challenges toward achieving a sustainable circular economy.

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Section: Potential Of Machine Learning In the Production Of Algal Bio...mentioning

confidence: 99%

Towards a Sustainable Circular Economy: Algae‐Based Bioplastics and the Role of Internet‐of‐Things and Machine Learning

Bin Abu Sofian,

Lim,

Manickam

et al. 2023

ChemBioEng Reviews

View full text Add to dashboard Cite

show abstract

“…A wide range of applications were developed using convolutional neural networks (CNNs), such as image classification, object detection, and image segmentation [20]. Defect detection using convolutional neural networks can be applied to several different objects [21][22][23]. Comparatively to traditional image processing methods, CNNs can automatically extract useful features from data without requiring complex feature designs to be handcrafted [24].…”

Section: Related Workmentioning

confidence: 99%

Using Object Detection Technology to Identify Defects in Clothing for Blind People

Rocha

Pinto

Machado

et al. 2023

Sensors

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Blind people often encounter challenges in managing their clothing, specifically in identifying defects such as stains or holes. With the progress of the computer vision field, it is crucial to minimize these limitations as much as possible to assist blind people with selecting appropriate clothing. Therefore, the objective of this paper is to use object detection technology to categorize and detect stains on garments. The defect detection system proposed in this study relies on the You Only Look Once (YOLO) architecture, which is a single-stage object detector that is well-suited for automated inspection tasks. The authors collected a dataset of clothing with defects and used it to train and evaluate the proposed system. The methodology used for the optimization of the defect detection system was based on three main components: (i) increasing the dataset with new defects, illumination conditions, and backgrounds, (ii) introducing data augmentation, and (iii) introducing defect classification. The authors compared and evaluated three different YOLOv5 models. The results of this study demonstrate that the proposed approach is effective and suitable for different challenging defect detection conditions, showing high average precision (AP) values, and paving the way for a mobile application to be accessible for the blind community.

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“…The system demonstrated good wear detection accuracy and can automatically evaluate the wear status of the slider to meet actual needs in the field. Bin Roslan et al [23] proposed a real-time detection and classification method of plastic surface defects based on deep learning to address slow deceleration and high labor costs. Tabernik et al [24] proposed a deep learning framework based on segmentation to recognize and divide surface anomalies.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Electronic Water Pump Shell Defect Detection Method Based on Improved YOLOv5s

Wu¹,

Wang²,

Fang³

et al. 2023

Computer Systems Science and Engineering

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For surface defects in electronic water pump shells, the manual detection efficiency is low, prone to misdetection and leak detection, and encounters problems, such as uncertainty. To improve the speed and accuracy of surface defect detection, a lightweight detection method based on an improved YOLOv5s method is proposed to replace the traditional manual detection methods. In this method, the MobileNetV3 module replaces the backbone network of YOLOv5s, depth-separable convolution is introduced, the parameters and calculations are reduced, and CIoU_Loss is used as the loss function of the boundary box regression to improve its detection accuracy. A dataset of electronic pump shell defects is established, and the performance of the improved method is evaluated by comparing it with that of the original method. The results show that the parameters and FLOPs are reduced by 49.83% and 61.59%, respectively, compared with the original YOLOv5s model, and the detection accuracy is improved by 1.74%, which is an indication of the superiority of the improved method. To further verify the universality of the improved method, it is compared with the results using the original method on the PASCALVOC2007 dataset, which verifies that it yields better performance. In summary, the improved lightweight method can be used for the real-time detection of electronic water pump shell defects.

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Real-Time Plastic Surface Defect Detection Using Deep Learning

Cited by 10 publications

References 15 publications

Towards a Sustainable Circular Economy: Algae‐Based Bioplastics and the Role of Internet‐of‐Things and Machine Learning

Towards a Sustainable Circular Economy: Algae‐Based Bioplastics and the Role of Internet‐of‐Things and Machine Learning

Using Object Detection Technology to Identify Defects in Clothing for Blind People

A Lightweight Electronic Water Pump Shell Defect Detection Method Based on Improved YOLOv5s

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