Transfer learning for automated optical inspection

Kim, Seunghyeon; Kim, Wooyoung; Noh, Yung‐Kyun; Park, Frank C.

doi:10.1109/ijcnn.2017.7966162

Cited by 76 publications

(47 citation statements)

References 11 publications

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“…The result suggests these strategies have remarkably leveraged performance, their model won the best-recorded mIOU (Mean Intersection over Union). In the practice of surface inspection [15], a fine-tuned ImageNet pretrained VGG-16 is used as a patch classifier on surface defect inspection. The experiment shows that fine-tuning all layers is the best valid way for models to achieve high accuracy.…”

Section: Transfer Learningmentioning

confidence: 99%

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A Compact Convolutional Neural Network for Surface Defect Inspection

Huang

Qiu

Wang

et al. 2020

Sensors

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The advent of convolutional neural networks (CNNs) has accelerated the progress of computer vision from many aspects. However, the majority of the existing CNNs heavily rely on expensive GPUs (graphics processing units). to support large computations. Therefore, CNNs have not been widely used to inspect surface defects in the manufacturing field yet. In this paper, we develop a compact CNN-based model that not only achieves high performance on tiny defect inspection but can be run on low-frequency CPUs (central processing units). Our model consists of a light-weight (LW) bottleneck and a decoder. By a pyramid of lightweight kernels, the LW bottleneck provides rich features with less computational cost. The decoder is also built in a lightweight way, which consists of an atrous spatial pyramid pooling (ASPP) and depthwise separable convolution layers. These lightweight designs reduce the redundant weights and computation greatly. We train our models on groups of surface datasets. The model can successfully classify/segment surface defects with an Intel i3-4010U CPU within 30 ms. Our model obtains similar accuracy with MobileNetV2 while only has less than its 1/3 FLOPs (floating-point operations per second) and 1/8 weights. Our experiments indicate CNNs can be compact and hardware-friendly for future applications in the automated surface inspection (ASI). pixels into regions, with the intention to emphasize the regions corresponding to surface defects. For example, Reference [6] firstly preprocesses images using convex optimization, then segments wood defects with Otsu segmentation method. Reference [12] segments fabric defects by the Sobel and the watershed algorithm.These traditional methods involve image preprocessing, feature extraction, feature reduction, and classifier selection, which needs the experience of experts. Besides, most surface defects are tiny and very similar to their background, even the inspection for a single type of defect can be very difficult for these traditional methods. Although traditional algorithms are designed for specific surfaces, effective measures such as recall, precision, and accuracy can barely achieve the standard of industrial applications. This long-existing bottleneck has been restraining the growth of ASI.Convolutional neural networks (CNNs) greatly stimulate the development of ASI [3,13-15] as deep learning methods in computer vision are achieving the state-of-the-art in recent years. As end-to-end solutions, CNNs also simplify the procedures of ASI. For example, a fully convolutional network (FCN) can segment surface defects by supervised learning [16], without any extra pre-process or post-process.A notorious limitation of CNNs is computation. In many cases, expensive GPUs with high computing capabilities are necessary to support large workloads. However, in considering manufacturing cost, most core computing resources of ASI platforms are mainly the low-power CPUs of industrial personal computers (IPC), or even FPGAs (field programmable gate array). To take advantage...

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Section: Transfer Learningmentioning

confidence: 99%

“…In Reference [15] a input image is cropped to 9 224 × 224 sub-images, and an ImageNet-pretrained VGG-16 [42] is fine-tuned. The whole image is classified by the vote result of 9 sub-images.…”

Section: Comparison Modelsmentioning

confidence: 99%

A Compact Convolutional Neural Network for Surface Defect Inspection

Huang

Qiu

Wang

et al. 2020

Sensors

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“…However, the dataset is small and may have problems of overfitting. In order to solve the problem that there is not enough labelled data in the defect detection, Kim [28] and others proposed a defect detection algorithm based on transfer learning. The paper transferred the weight parameters of other models to the current defect detection model to achieve sharing of weights and easing overfitting.…”

Section: Related Work On Solar Cell Surface Detectionmentioning

confidence: 99%

Solar cell surface defect inspection based on multispectral convolutional neural network

et al. 2018

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Similar and indeterminate defect detection of solar cell surface with heterogeneous texture and complex background is a challenge of solar cell manufacturing. The traditional manufacturing process relies on human eye detection which requires a large number of workers without a stable and good detection effect. In order to solve the problem, a visual defect detection method based on multi-spectral deep convolutional neural network is designed in this paper. Firstly, a selected convolutional neural network (CNN)model is established. By adjusting the depth and width of the model, the influence of model depth and kernel size on the recognition result is evaluated. The optimal convolutional neural network model structure is selected. Secondly, the light spectrum features of solar cell color image are analyzed. It is found that a variety of defects exhibited different distinguishable characteristics in different spectral bands. Thus, a multi-spectral convolutional neural network model is constructed to enhance the discrimination ability of the model to distinguish between complex texture background features and defect features. Finally, some experimental results and K-fold cross validation show that the multi-spectral deep convolutional neural network model can effectively detect the solar cell surface defects with higher accuracy and greater adaptability. The accuracy of defect recognition reaches 94.30%. Applying such an algorithm can increase the efficiency of solar cell manufacturing and make the manufacturing process smarter.

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“…Metallic defects detection has been exploited to satisfy predefined quality requirements for the industry. Therefore, metallic surface defect detection has attracted increasing interest in recent years and has achieved a positive improvement for the quality control in industrial applications [1]. However, metallic surface defect detection is easily influenced by many environmental factors such as illumination, light reflection, and metal material.…”

Section: Introductionmentioning

confidence: 99%

Deep Metallic Surface Defect Detection: The New Benchmark and Detection Network

Duan

Jiajia

et al. 2020

Sensors

211

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Metallic surface defect detection is an essential and necessary process to control the qualities of industrial products. However, due to the limited data scale and defect categories, existing defect datasets are generally unavailable for the deployment of the detection model. To address this problem, we contribute a new dataset called GC10-DET for large-scale metallic surface defect detection. The GC10-DET dataset has great challenges on defect categories, image number, and data scale. Besides, traditional detection approaches are poor in both efficiency and accuracy for the complex real-world environment. Thus, we also propose a novel end-to-end defect detection network (EDDN) based on the Single Shot MultiBox Detector. The EDDN model can deal with defects with different scales. Furthermore, a hard negative mining method is designed to alleviate the problem of data imbalance, while some data augmentation methods are adopted to enrich the training data for the expensive data collection problem. Finally, the extensive experiments on two datasets demonstrate that the proposed method is robust and can meet accuracy requirements for metallic defect detection.

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Transfer learning for automated optical inspection

Cited by 76 publications

References 11 publications

A Compact Convolutional Neural Network for Surface Defect Inspection

A Compact Convolutional Neural Network for Surface Defect Inspection

Solar cell surface defect inspection based on multispectral convolutional neural network

Deep Metallic Surface Defect Detection: The New Benchmark and Detection Network

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