Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD

Li, Yiting; Huang, Haisong; Xie, Qingsheng; Yao, Liguo; Chen, Qipeng

doi:10.3390/app8091678

Cited by 243 publications

(114 citation statements)

References 31 publications

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“…14 With the rise of the interest in deep learning, this specific technology was also applied to a wide range of AVI tasks, for example, structural inspection, 15 surface defect detection and recognition, 16 defect classification system, 17 and others. [18][19][20][21] Even design tools for the creation of deep convolutional networks for AVI were developed. 22 To conclude, deep learning is being applied to a wide range of tasks inside the AVI field, starting with detection, classification, and recognition for the purpose of defect inspection and finishing with full-scale automated production support.…”

Section: Related Workmentioning

confidence: 99%

Classification using a three-dimensional sensor in a structured industrial environment

Mikhailov

Jovančević²,

Mokhtari³

2020

J. Electron. Imag.

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Usage of a three-dimensional (3-D) sensor and point clouds provides various benefits over the usage of a traditional camera for industrial inspection. We focus on the development of a classification solution for industrial inspection purposes using point clouds as an input. The developed approach employs deep learning to classify point clouds, acquired via a 3-D sensor, the final goal being to verify the presence of certain industrial elements in the scene. We possess the computer-aided design model of the whole mechanical assembly and an in-house developed localization module provides initial pose estimation from which 3-D point clouds of the elements are inferred. The accuracy of this approach is proved to be acceptable for industrial usage. Robustness of the classification module in relation to the accuracy of the localization algorithm is also estimated. Fig. 1 (a) Robot-based inspection system with three cameras mounted on an end effector and (b) Ensenso N35 3-D sensor.

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Section: Related Workmentioning

confidence: 99%

Classification using a three-dimensional sensor in a structured industrial environment

Mikhailov

Jovančević²,

Mokhtari³

2020

J. Electron. Imag.

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“…Thus far, many successful architectures have been proposed for CNN, which include DenseNet [46], ResNet [47], VGG16, RCNN, etc., and some of them have been applied to defect detection [37][38][39][40][41][42][43][44][45]. VGG16 architecture is employed for pavement crack detection in [43].…”

Section: Detection Scheme Based On Cnnmentioning

confidence: 99%

“…VGG16 architecture is employed for pavement crack detection in [43]. Single shot multibox detector network is adopted to detect surface defects of container [44]. RCNN architecture is used for polymeric polarizer detection of liquid crystal display panel [45].…”

Section: Detection Scheme Based On Cnnmentioning

confidence: 99%

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Blister Defect Detection Based on Convolutional Neural Network for Polymer Lithium-Ion Battery

Xie

Zhang

2019

Applied Sciences

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To ensure the quality and reliability of polymer lithium-ion battery (PLB), automatic blister defect detection instead of manual detection is developed in the production of PLB cell sheets. A convolutional neural network (CNN) based detection method is proposed to detect blister in cell sheets employing cell sheet images. An improved architecture for dense block and a learning method based on optimization of learning rate are discussed. The proposed method was superior to other machine learning based methods when the classification performance and confusion matrix were compared in experiments. The proposed CNN method had the best defect detection performance and real-time performance for industry field application. Keywords: defect detection; polymer lithium-ion battery; convolutional neural network; deep learning; blister defect; flower pollination algorithm IntroductionThe application of lithium-ion batteries has changed consumer electronic products, which greatly reduce the weight and volume of mobile phones, notebooks, and other portable products. Lithium-ion batteries have been widely studied, including model, optimal design, and so on [1][2][3][4][5][6]. At present, the most commonly used lithium-ion battery is polymer lithium-ion battery (PLB). The cathode materials of PLB are commonly lithium cobalt acid, lithium manganese acid, ternary materials or lithium iron phosphate. The anode materials usually use carbon materials, such as artificial graphite, natural graphite, intermediate phase carbon microspheres, petcoke, carbon fiber, pyrolysis resin carbon and so on. C-rate is the measurement of the charge and discharge current with respect to its nominal capacity. At present, most PLB use polymer gel electrolyte instead of liquid electrolyte, which makes PLB have the advantages of thinning, arbitrary area and arbitrary shape. These characteristics improve the capacity of PLB, and PLB has the characteristics of miniaturization, thinning, and light quantification. PLB has been widely used in portable electronic equipment, and it is gradually being applied to more fields. With the application of PLB in more and more electronic products, the quality of PLB has more and more influence on the quality of electronic products. The quality of PLB is critical to the quality and reliability of electronic products.Recently, an automatic line of PLB has been developed in Dongsheng Energy Corporation, Weihai, China. The PLB has a voltage of 48 V, a charging current of 2-3 A, and nominal capacity of 16 AH. The anode of PLB is a conductive high molecular polymer, the catrode is graphite, and a colloidal polymer electrolyte is used. During the production process of the PLB, several cathode pieces, anode pieces, and separator pieces are combined to produce a cell sheet. Great battery capacity can be provided with more cell sheets combined together. Thus, the quality inspection of cell sheets in automated production lines is essential to the quality of the final battery product. The cell sheet defect needs to be detected to ens...

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“…Then, a classifier was used to classify defects. Li et al [31] proposed a surface defect detection model based on the SSD network that was combined with a MobileNet to detect the sealing surface of an oil chili to achieve real-time and accurate detection. The Hough circle transform was applied to detect the oil chili.…”

mentioning

confidence: 99%

Apparatus and Method of Defect Detection for Resin Films

et al. 2020

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A defect inspection of resin films involves processes of detecting defects, size measuring, type classification and reflective action planning. It is not only a process requiring heavy investment in workforce, but also a tension between quality assurance with a 50-micrometer tolerance and visibility of the naked eye. To solve the difficulties of the workforce and time consumption processes of defect inspection, an apparatus is designed to collect high-quality images in one shot by leveraging a large field-of-view microscope at 2K resolution. Based on the image dataset, a two-step method is used to first locate possible defects and predict their types by a defect-shape-based deep learning model using the LeNet-5-adjusted network. The experimental results show that the proposed method can precisely locate the position and accurately inspect the fine-grained defects of resin films.Appl. Sci. 2020, 10, 1206 2 of 23 3.In some cases, the manufacturer may want to tolerate no defect larger than 25 µm. However, human vision is limited to objects larger than approximately 50 µm; this limit is not acceptable for high-quality products. 4.Defect detection is not immediate. Using human vision for the inspection process is considerably time consuming and inefficient. The average number of plastic resin films that can be checked by a person in a day is approximately 12 to 15.Automatic procedures, including machine vision-based methods of defect detection, have gained considerable popularity for their high speed, high precision, and real-time performance. Several strategies for defect detection have been explored in numerous studies. These machine vision-based methods are used in the inspection of many industrial products, such as metal [1], fabric [2], steel [3], car surfaces [4], and light-emitting diode chips [5]. The methods of surface defect detection can be divided into three types, as depicted in Figure 1, namely traditional image processing, feature extraction with machine learning, and deep learning methods. Appl. Sci. 2020, 10, 1206 2 of 23 miss some defects. Furthermore, it is nearly impossible to standardize human vision; each inspector's eyesight will have its own peculiarities. 3. In some cases, the manufacturer may want to tolerate no defect larger than 25 μm. However, human vision is limited to objects larger than approximately 50 μm; this limit is not acceptable for high-quality products. 4. Defect detection is not immediate. Using human vision for the inspection process is considerably time consuming and inefficient. The average number of plastic resin films that can be checked by a person in a day is approximately 12 to 15.Automatic procedures, including machine vision-based methods of defect detection, have gained considerable popularity for their high speed, high precision, and real-time performance. Several strategies for defect detection have been explored in numerous studies. These machine vision-based methods are used in the inspection of many industrial products, such as metal [1], fabric [2], steel [3],...

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Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD

Cited by 243 publications

References 31 publications

Classification using a three-dimensional sensor in a structured industrial environment

Classification using a three-dimensional sensor in a structured industrial environment

Blister Defect Detection Based on Convolutional Neural Network for Polymer Lithium-Ion Battery

Apparatus and Method of Defect Detection for Resin Films

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