Improved MobileNetV2-SSDLite for automatic fabric defect detection system based on cloud-edge computing

Zhang, Jiaqi; Jing, Junfeng; Lu, Pengwen; Song, Shaojun

doi:10.1016/j.measurement.2022.111665

Cited by 35 publications

(15 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, spurred by the rapid progress in deep learning technology, researchers across the globe have delved extensively into the utilization of deep learning models, including convolutional neural networks, within the realm of target detection, resulting in a multitude of successful applications [13][14][15]. Target detection algorithms can be broadly classified into two main categories: single-stage target detection algorithms and dual-stage target detection algorithms.…”

Section: Related Workmentioning

confidence: 99%

An efficient and accurate surface defect detection method for quality supervision of wood panels

Yi,

Luo,

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

The wood panel processing sector is integral to the landscape of industrial manufacturing, and automated detection of wood panel surface defects has become an important guarantee for improving the efficiency and quality of processing production. However, due to the diverse scales and shapes of wood panel surface defects, as well as their complex and varied colors and texture characteristics, the efforts to efficiently and accurately detect surface defects in wood panels through existing methods have fallen short. Therefore, the paper proposes an enhanced YOLOx-tiny deep learning network for wood panel surface defect detection. We introduce new modules MFFM and CFEM, instead of the original SPP and Bottleneck modules to enhance key feature extraction and reduce the number of computational parameters.The experimental results conducted on the self-constructed wood panel surface defects dataset show that the mAP of our proposed method is 95.01%, which is 9.58% higher than the original YOLOx-tiny network model, and the defects recall is 91.46%, which is 13.21% higher compared to the original network. Meanwhile, the method is able to reduce 12.22% of computational parameters and 12.67% of GFLOPs, which effectively improves the efficiency of the detection of surface defects on wood panels. In summary, the proposed intelligent surface defect detection approach for wood panels, which utilizes an enhanced YOLOx-tiny deep learning network, has yielded notable outcomes in enhancing both accuracy and efficiency. This method holds significant practical relevance for the wood panel manufacturing sector, offering the potential to enhance both production efficiency and quality. It also explores the automation and intelligent technology in the process of man-made board processing, which provides a valuable reference for the research in related fields.

show abstract

Section: Related Workmentioning

confidence: 99%

An efficient and accurate surface defect detection method for quality supervision of wood panels

Yi,

Luo,

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…[2][3][4][5][6][7][8][9][10] And some researchers have designed a string of defect detection systems based on machine vision methods for specific products and defects. [11][12][13][14][15][16][17][18][19][20][21] Textile fabrics defect was detected via optimal Gabor filter by Jing et al 2 In, 3 a method for detecting fabric defects using a thermal camera was presented. A method based on principal component analysis was proposed by Kazim to achieve dimensionality reduction-based classification of fleece fabric-based images.…”

Section: Introductionmentioning

confidence: 99%

“…In, 19 a tunnel surface detection system was proposed, which used a Faster-RCNN based multi-scale feature fusion network to achieve real-time tunnel image defect detection. A collaborative cloud edge fabric defect detection system based on the MobileNetV-SSDLite method was presented in, 20 which improved the detection accuracy of small defects and complex background images in resource-constrained situations. A wire arc additive manufacturing defect detection system based on incremental learning was proposed by Li et al 21 As the needs of society are constantly being updated, the new composite materials are playing an increasingly important role in everyday life.…”

Section: Introductionmentioning

confidence: 99%

A defect detection system of glass tube yarn based on machine vision

Bao

Jing

Xie

2023

Journal of Industrial Textiles

Self Cite

View full text Add to dashboard Cite

Tube yarn is also called glass fiber spun yarn. Due to the excellent properties of glass fiber, various industrial products based on glass fiber are used in a variety of industries. As the most obvious factor affecting the quality of products, quality detection of glass fiber yarns is very important for companies. Due to traditional defect detection relies on the experience and subjective factors of workers, which makes many different views on the same defect. Some traditional methods limit the solution to specific types of defects and do not accurately detect various defects. In this paper, we propose a comprehensive defect detection system of tube yarn via combining machine vision and deep learning methods. In whole system, we inspect the weight through a weight sensor firstly. Then, we propose a multi-scale cross-fusion attention module to improve the MobileNetV2, and combine with machine vision image feature extraction method for hairiness detection. Finally, the modified MobileNetV2 network is used as the backbone of YOLOX network, making the YOLOX is lighter and achieve more efficiently stain detection of tube yarn. Then, the detection results are used to determine whether the glass tube yarn has passed. In addition, we establish an effective and sufficient amount of tube yarn defects dataset. The experimental results show that the proposed hairiness detection algorithm achieve 96% accuracy with 160+ FPS, and the surface stain detection algorithm achieve 0.89 mAP with 71+ FPS on the tube yarn dataset. The system is efficient, precise, and can be applied to actual production.

show abstract

“…In recent years, deep learning has developed rapidly and is widely used in defect detection in a variety of applications [6][7][8][9][10][11]. It is gradually replacing defect detection methods based on feature engineering.…”

Section: Introductionmentioning

confidence: 99%

Crack identification of automobile steering knuckle fluorescent penetrant inspection based on deep convolutional generative adversarial networks data enhancement

et al. 2022

View full text Add to dashboard Cite

As a key safety component of automobiles, automobile steering knuckles must be subjected to strict quality control. Currently, the identification of cracks in finished products primarily relies on manual identification of fluorescent penetrant detection. Owing to the complex shape of the workpiece, the interference of the displayed image and the small sample size, the accuracy of the automatic discrimination result of the fluorescent penetrant detection image is directly reduced. Therefore, this study proposed a data augmentation method based on deep convolutional generative adversarial networks (DCGAN) for crack identification in automotive steering knuckle fluorescent penetration inspection images. An image acquisition platform was built for fluorescence penetration detection of automobile steering knuckles, and fluorescence display images of various parts of the workpiece were collected. Based on the feature analysis of the displayed image, the image was preprocessed to suppress relevant interference and extract crack candidate regions. Further, using the original crack image to train DCGAN, several crack image samples were generated, the ResNet network was trained with the expanded dataset, and the extracted candidate regions were identified. Finally, the experimental results show that the recall rate of the crack recognition method used in this paper is 95.1%, and the accuracy rate is 90.8%, which can better identify the crack defects in the fluorescent penetrant inspection image, compared with the non-generative data enhancement method.

show abstract

Improved MobileNetV2-SSDLite for automatic fabric defect detection system based on cloud-edge computing

Cited by 35 publications

References 21 publications

An efficient and accurate surface defect detection method for quality supervision of wood panels

An efficient and accurate surface defect detection method for quality supervision of wood panels

A defect detection system of glass tube yarn based on machine vision

Crack identification of automobile steering knuckle fluorescent penetrant inspection based on deep convolutional generative adversarial networks data enhancement

Contact Info

Product

Resources

About