Defect Detection In Images Research Articles

Application of YOLOv5 Based on Attention Mechanism and Receptive Field in Identifying Defects of Thangka Images

Aiming at the problems of target detection network in the defect detection field of thangka images with complex background colors, such as poor small target detection effect, insufficient feature information extraction, prone to error detection and leak detection, and low accuracy of defect detection, this paper proposed the YOLOv5 defect detection algorithm combining attention mechanism and receptive field. First of all, the Backbone network is used for feature extraction, integrating attention mechanism to represent different features, so that the network can fully extract the texture and semantic features of the defect area, and the extracted features are weighted and fused to reduce information loss. Secondly, a weighted fusion of features of different dimensions is transferred by the Neck network, and the combination of FPN and PAN is used to realize the fusion of semantic features and texture features of different layers and to locate the defect target more accurately. Finally, while replacing the GIoU loss function with CIoU, the receptive field is added to the network, so that the algorithm uses a four-channel detection mechanism to expand the detection range of receptive fields, and fuses semantic information between different network layers, so as to achieve fast location and more refined processing of small targets. The experimental results show that compared with the original YOLOv5 network, the detection accuracy of YOLOV5-scSE and YOLOV5-CA networks proposed in this paper has improved by 9.96 percentage points and 12.22 percentage points respectively, and the verification index has been significantly improved. It can quickly and more accurately identify and locate the location of the defect area and has a stronger ability to generalize the defect category, which greatly improves the accuracy of thangka image defect detection.

Defect detection in atomic-resolution images via unsupervised learning with translational invariance

Crystallographic defects can now be routinely imaged at atomic resolution with aberration-corrected scanning transmission electron microscopy (STEM) at high speed, with the potential for vast volumes of data to be acquired in relatively short times or through autonomous experiments that can continue over very long periods. Automatic detection and classification of defects in the STEM images are needed in order to handle the data in an efficient way. However, like many other tasks related to object detection and identification in artificial intelligence, it is challenging to detect and identify defects from STEM images. Furthermore, it is difficult to deal with crystal structures that have many atoms and low symmetries. Previous methods used for defect detection and classification were based on supervised learning, which requires human-labeled data. In this work, we develop an approach for defect detection with unsupervised machine learning based on a one-class support vector machine (OCSVM). We introduce two schemes of image segmentation and data preprocessing, both of which involve taking the Patterson function of each segment as inputs. We demonstrate that this method can be applied to various defects, such as point and line defects in 2D materials and twin boundaries in 3D nanocrystals.

Infrared computer vision in non-destructive imaging: Sharp delineation of subsurface defect boundaries in enhanced truncated correlation photothermal coherence tomography images using K-means clustering

This work investigates the non-destructive detection of defects in thermal images of industrial materials based on segmentation of images generated using enhanced truncated-correlation photothermal coherence tomography (eTC-PCT). eTC-PCT is an active infrared thermography modality, which is being applied to the field of non-destructive testing (NDT) and in biomedical & dental thermophotonic imaging. In this report, we combine eTC-PCT with a computer vision algorithm to sharply delineate holes and manufacturing defects (cracks) inside industrial materials. To this end, the eTC-PCT reconstructed image is processed through three consecutive algorithm stages: A threshold selection filter is followed by filtered image segmentation using the K-means algorithm (clustering method) and the outcome is applied to the delineation of (otherwise blurred) discontinuity boundaries by means of the Canny edge detection algorithm. The role of each method is described and it is demonstrated that the combination of these three algorithms is optimal for achieving significant delineation enhancement (sharpness) of blind hole and crack boundaries in industrial materials.

Deep learning from imbalanced data for automatic defect detection in multicrystalline solar wafer images

This paper presents a deep learning method for automated defect inspection in multicrystalline solar wafer surfaces. A multicrystalline solar wafer contains local crystal grains with random shapes, sizes and gray-values. It shows a heterogeneous texture in the surface, and makes the automated optical inspection task very challenging. The conventional machine vision needs individually handcraft specific texture features for different types of defects in the silicon solar wafer. The deep learning technique is an end-to-end training. It can learn discriminant features and classification simultaneously. However, it needs a large amount of defect-free and defective samples for the model training. The defect samples are generally not sufficient in the manufacturing process. A generative adversarial network based model is used to generate synthetic defect samples from a small set of real defects. The synthesized defective and true defect-free samples are then used to train a convolutional neural network (CNN) model for defect detection. It overcomes the imbalanced class data problem arising in the CNN model training. The proposed method is compared with the commonly-used learning strategies for imbalanced data. Experimental results show that the proposed deep learning approach can be applied to a variety of defect types found in the multicrystalline solar wafer surface.

A Lightweight Deep Network for Defect Detection of Insert Molding Based on X-ray Imaging.

Aiming at the abnormality detection of industrial insert molding processes, a lightweight but effective deep network is developed based on X-ray images in this study. The captured digital radiography (DR) images are firstly fast guide filtered, and then a multi-task detection dataset is constructed using an overlap slice in order to improve the detection of tiny targets. The proposed network is extended from the one-stage target detection method of yolov5 to be applicable to DR defect detection. We adopt the embedded Ghost module to replace the standard convolution to further lighten the model for industrial implementation, and use the transformer module for spatial multi-headed attentional feature extraction to perform improvement on the network for the DR image defect detection. The performance of the proposed method is evaluated by consistent experiments with peer networks, including the classical two-stage method and the newest yolo series. Our method achieves a mAP of 93.6%, which exceeds the second best by 3%, with robustness sufficient to cope with luminance variations and blurred noise, and is more lightweight. We further conducted ablation experiments based on the proposed method to validate the 32% model size reduction owing to the Ghost module and the detection performance enhancing effect of other key modules. Finally, the usability of the proposed method is discussed, including an analysis of the common causes of the missed shots and suggestions for modification. Our proposed method contributes a good reference solution for the inspection of the insert molding process.

Research on Image Defect Detection of Silicon Panel Based on Prewitt and Canny Operator

The silicon panel is the core component of photovoltaic power generation, whose surface quality is related to its service life and power generation efficiency. However, microcracks, fragments, incomplete welding, broken grids, and other defects often occur in industrial production. The edge detection algorithm is usually used to detect defects in silicon panels, but the common edge detection algorithm has an impact on defect detection because of the grid shadow of the panel. The current mainstream defect detection algorithm based on convolutional neural network requires a large number of positive and negative samples of image data sets for pretraining the model, which consumes a lot of time and GPU computing power, and the steps are cumbersome. To solve the problem, a defect detection method based on Prewitt and Canny operators is proposed in this article. In this method, Prewitt and Canny operators are combined to eliminate the effect of grids on the detection. The microcrack defects and their specific positions can be detected efficiently and intuitively, therefore improving the detection accuracy. The experimental results indicate that the purity and integrity of the defect profile of the image processed by the algorithm are greatly improved. The foreground edge is clear, and the defect recognition accuracy is higher, which effectively prevent the impact of grid shadow on weld testing.

Automatic detection of crystallographic defects in STEM images by unsupervised learning with translational invariance

Automatic detection of crystallographic defects in STEM images by unsupervised learning with translational invariance

Deep Learning for the Detection and Recognition of Rail Defects in Ultrasound B-Scan Images

Rail defect detection is crucial to rail operations safety. Addressing the problem of high false alarm rates and missed detection rates in rail defect detection, this paper proposes a deep learning method using B-scan image recognition of rail defects with an improved YOLO (you only look once) V3 algorithm. Specifically, the developed model can automatically position a box in B-scan images and recognize EFBWs (electric flash butt welds), normal bolt holes, BHBs (bolt hole breaks), and SSCs (shells, spalling, or corrugation). First, the network structure of the YOLO V3 model is modified to enlarge the receptive field of the model, thus improving the detection accuracy of the model for small-scale objects. Second, B-scan image data are analyzed and standardized. Third, the initial training parameters of the improved YOLO V3 model are adjusted. Finally, the experiments are performed on 453 B-scan images as the test data set. Results show that the B-scan image recognition model based on the improved YOLO V3 algorithm reached high performance in its precision. Additionally, the detection accuracy and efficiency are improved compared with the original model and the final mean average precision can reach 87.41%.

RETRACTED: Artificial neural network for defect detection in CT images of wood

• New image processing technique is proposed to increase the volume of sets. • The recognition rate of the network was found to be 98.7% (100 epochs). • Results help to automate and speed up the defect recognition in CT images. • Results can be useful in designing software applications for CT scanners. The timely detection of defects in wood helps optimize operation of sawmills and find effective log processing solutions. This paper aims to develop effective method for automated wood defect detection and recognition in CT images using a multiple-layer convolutional neural network and a reinforcement learning strategy. The data augmentation technique was proposed to increase the volume of training, validation, and test sets. The network can achieve sufficient accuracy up to 98.7% at a total set of 500 images. The study shows a direct non-linear relationship between the dimension of the training set and the recognition rate. The results of calculating performance metrics for the developed method indicate the high accuracy of the ANN prediction model. The study results will be useful in designing software applications for industrial and laboratory CT scanners that are used in lumber production and R&D centers. The proposal can be improved to perform wood defect detection and recognition in color and 3D CT images of logs.

Generative Adversarial Networks to Improve the Robustness of Visual Defect Segmentation by Semantic Networks in Manufacturing Components

This paper describes the application of Semantic Networks for the detection of defects in images of metallic manufactured components in a situation where the number of available samples of defects is small, which is rather common in real practical environments. In order to overcome this shortage of data, the common approach is to use conventional data augmentation techniques. We resort to Generative Adversarial Networks (GANs) that have shown the capability to generate highly convincing samples of a specific class as a result of a game between a discriminator and a generator module. Here, we apply the GANs to generate samples of images of metallic manufactured components with specific defects, in order to improve training of Semantic Networks (specifically DeepLabV3+ and Pyramid Attention Network (PAN) networks) carrying out the defect detection and segmentation. Our process carries out the generation of defect images using the StyleGAN2 with the DiffAugment method, followed by a conventional data augmentation over the entire enriched dataset, achieving a large balanced dataset that allows robust training of the Semantic Network. We demonstrate the approach on a private dataset generated for an industrial client, where images are captured by an ad-hoc photometric-stereo image acquisition system, and a public dataset, the Northeastern University surface defect database (NEU). The proposed approach achieves an improvement of 7% and 6% in an intersection over union (IoU) measure of detection performance on each dataset over the conventional data augmentation.

Deep Learning Approach for Brain Tumor Classification

Tumors are complex. There are a lot of variations in sizes and location of tumor. This makes it really hard for complete understanding of tumor. Brain tumour is the abnormal growth of cells inside the brain cranium which limits the functioning of brain. Now a days, medical images processing is a most challenging and developing field. Automated detection of tumor in MRI is extremely crucial because it provides information about abnormal tissues which is important for planning treatment. The conventional method for defect detection in resonance brain images is time consuming. So, automated tumor detection methods are developed because it would save radiologist time and acquire a tested accuracy. The MRI brain tumor detection is complicated task due to complexity and variance of tumors.There are many previously implemented approaches on detecting these kinds of brain tumors. In this paper, we used and implement Convolutional Neural Network (CNN) which is one among the foremost widely used deep learning architectures for classifying a brain tumor into four types. i.e Glioma , Meningioma, Pituitary and No tumour. CNN may be used to effectively locate most cancers cells in brain via MRI. classification.

Defect detection and quantification in electroluminescence images of solar PV modules using U-net semantic segmentation

Electroluminescence (EL) images enable defect detection in solar photovoltaic (PV) modules that are otherwise invisible to the naked eye, much the same way an x-ray enables a doctor to detect cracks and fractures in bones. The prevalence of multiple defects, e.g. micro cracks, inactive regions, gridline defects, and material defects, in PV module can be quantified with an EL image. Modern, deep learning techniques for computer vision can be applied to extract the useful information contained in the images on entire batches of PV modules. Defect detection and quantification in EL images can improve the efficiency and the reliability of PV modules both at the factory by identifying potential process issues and at the PV plant by identifying and reducing the number of faulty modules installed. In this work, we train and test a semantic segmentation model based on the u-net architecture for EL image analysis of PV modules made from mono-crystalline and multi-crystalline silicon wafer-based solar cells. This work is focused on developing and testing a deep learning method for computer vision that is independent of the equipment used to generate the EL images, independent of the wafer-based module design, and independent of the image quality.

Defect Detection in Atomic Resolution Transmission Electron Microscopy Images Using Machine Learning

Point defects play a fundamental role in the discovery of new materials due to their strong influence on material properties and behavior. At present, imaging techniques based on transmission electron microscopy (TEM) are widely employed for characterizing point defects in materials. However, current methods for defect detection predominantly involve visual inspection of TEM images, which is laborious and poses difficulties in materials where defect related contrast is weak or ambiguous. Recent efforts to develop machine learning methods for the detection of point defects in TEM images have focused on supervised methods that require labeled training data that is generated via simulation. Motivated by a desire for machine learning methods that can be trained on experimental data, we propose two self-supervised machine learning algorithms that are trained solely on images that are defect-free. Our proposed methods use principal components analysis (PCA) and convolutional neural networks (CNN) to analyze a TEM image and predict the location of a defect. Using simulated TEM images, we show that PCA can be used to accurately locate point defects in the case where there is no imaging noise. In the case where there is imaging noise, we show that incorporating a CNN dramatically improves model performance. Our models rely on a novel approach that uses the residual between a TEM image and its PCA reconstruction.

Attention U-Net with Feature Fusion Module for Robust Defect Detection

U-Net shows a remarkable performance and makes significant progress for segmentation task in medical images. Despite the outstanding achievements, the common case of defect detection in industrial scenes is still a challenging task, due to the noisy background, unpredictable environment, varying shapes and sizes of the defects. Traditional U-Net may not be suitable for low-quality images with low illumination and corruption, which are often presented in the practical collections in real-world scenes. In this paper, we propose an attention U-Net with feature fusion module for combining multi-scale features to detect the defects in noisy images automatically. Feature fusion module contains convolution kernels of different scales to capture shallow layer features and combine them with the high-dimensional features. Meanwhile, attention gates are used to enhance the robustness of skip connection between the feature maps. The proposed method is evaluated on two datasets. The best precision rate and MIoU of defect detection are 95.6% and 92.5%. The best F-score of concrete crack detection is 95.0%. Experimental results show that the proposed approach achieves promising results in both datasets. It demonstrates that our approach consistently outperforms other U-Net-based approaches for defect detection in low-quality images. Experimental results have shown the possibility of developing a mixture system that can be deployed in many applications, such as remote sensing image analysis, earthquake disaster situation assessment, and so on.

Segmented Embedded Rapid Defect Detection Method for Bearing Surface Defects

The rapid development of machine vision has prompted the continuous emergence of new detection systems and algorithms in surface defect detection. However, most of the existing methods establish their systems with few comparisons and verifications, and the methods described still have various problems. Thus, an original defect detection method: Segmented Embedded Rapid Defect Detection Method for Surface Defects (SERDD) is proposed in this paper. This method realizes the two-way fusion of image processing and defect detection, which can efficiently and accurately detect surface defects such as depression, scratches, notches, oil, shallow characters, abnormal dimensions, etc. Besides, the character recognition method based on Spatial Pyramid Character Proportion Matching (SPCPM) is used to identify the engraved characters on the bearing dust cover. Moreover, the problem of characters being cut in coordinate transformation is solved through Image Self-Stitching-and-Cropping (ISSC). This paper adopts adequate real image data to verify and compare the methods and proves the effectiveness and advancement through detection accuracy, missing alarm rate, and false alarm rate. This method can provide machine vision technical support for bearing surface defect detection in its real sense.

Deep Learning Approaches on Defect Detection in High Resolution Aerial Images of Insulators.

By detecting the defect location in high-resolution insulator images collected by unmanned aerial vehicle (UAV) in various environments, the occurrence of power failure can be timely detected and the caused economic loss can be reduced. However, the accuracies of existing detection methods are greatly limited by the complex background interference and small target detection. To solve this problem, two deep learning methods based on Faster R-CNN (faster region-based convolutional neural network) are proposed in this paper, namely Exact R-CNN (exact region-based convolutional neural network) and CME-CNN (cascade the mask extraction and exact region-based convolutional neural network). Firstly, we proposed an Exact R-CNN based on a series of advanced techniques including FPN (feature pyramid network), cascade regression, and GIoU (generalized intersection over union). RoI Align (region of interest align) is introduced to replace RoI pooling (region of interest pooling) to address the misalignment problem, and the depthwise separable convolution and linear bottleneck are introduced to reduce the computational burden. Secondly, a new pipeline is innovatively proposed to improve the performance of insulator defect detection, namely CME-CNN. In our proposed CME-CNN, an insulator mask image is firstly generated to eliminate the complex background by using an encoder-decoder mask extraction network, and then the Exact R-CNN is used to detect the insulator defects. The experimental results show that our proposed method can effectively detect insulator defects, and its accuracy is better than the examined mainstream target detection algorithms.

Deep-Learning Based Segmentation Algorithm for Defect Detection in Magnetic Particle Testing Images *

Deep-Learning Based Segmentation Algorithm for Defect Detection in Magnetic Particle Testing Images *

Surface Defect Inspection in Images Using Statistical Patches Fusion and Deeply Learned Features

Defect detection in images is a challenging task due to the existence of tiny and noisy patterns on surface images. To tackle this challenge, a defect detection approach is proposed in this paper using statistical data fusion. First, the proposed approach breaks a large image that contains multiple separate defects into smaller overlapping patches to detect the existence of defects in each patch, using the conventional convolutional neural network approach. Then, a statistical data fusion approach is proposed to maintain the spatial coherence of cracks in the image and aggregate the information extracted from overlapping patches to enhance the overall performance and robustness of the system. The proposed approach is evaluated using three benchmark datasets to demonstrate its superior performance in terms of both individual patch inspection and the whole image inspection.

Detection of Pin Defects in Aerial Images Based on Cascaded Convolutional Neural Network

Pins are standard fasteners in power transmission lines, and the hidden dangers of pins falling off dramatically affects their safe operation. If a pin is missed, it is called pin defects in this paper. As the pin is a small target and has a complex background, traditional detection algorithms were used to identify pin defects from aerial images which suffer from poor accuracy and low efficiency. This paper proposed a target detection method based on cascaded convolutional neural networks. First, a small-scale shallow full convolutional neural network was used to obtain the region of interest; then, a deeper convolutional neural network conducted target classification and positioning on the obtained region of interest. Next, a nonlinear multilayer perceptron was introduced, the convolution kernel was decomposed, and the multi-scale feature maps were fused. At this point, an angle variable was added to the classification cross-entropy loss function. Multi-task learning and offline hard sample mining strategies were used in the training phase. The proposed method was tested on a self-built pin dataset and the remote sensing image RSOD dataset, and the experimental results proved its effectiveness. Our method can accurately identify pin defects in aerial images, thereby solving the engineering application problem of pin defect detection in transmission lines.

Tiny Defect Detection in High-Resolution Aero-Engine Blade Images via a Coarse-to-Fine Framework

This article studies the problem of aero-engine blade surface defect detection in large images. The effective method for aero-engine blade surface inspection for this real application is currently lacking since most defects are relatively small. Therefore, the task of aero-engine blade surface defect detection is mainly implemented by experienced operators, which is subjective and time-consuming. Moreover, it is hard to fit the requirements of higher accuracy and efficiency manually. Therefore, an effective and efficient method for aero-engine blade surface defect detection is demanded. To achieve this, we propose a vision-based framework in this study to detect defects in a coarse-to-fine manner. First, the captured raw images are with a high resolution of $2448\times 2048$ to ensure the accuracy of defect detection. The raw images are then cropped into smaller regions and fed into our deep convolution neural network (DCNN) to learn features with high representation. Next, the coarse classifier module is proposed to filter most background regions out. Finally, the defects are located and classified by a fine detector module in the defective images, which are selected by the coarse classifier module. Instead of directly applying a detector, our coarse-to-fine framework can effectively save computation and improve accuracy. In addition, the coarse-to-fine framework can be trained in an end-to-end manner. Compared with classical methods for object detection, our method also achieves state-of-the-art performance for aero-engine blade surface defect detection in terms of accuracy and efficiency. Furthermore, our framework has been applied for practical application in many aero-engine blade production lines.