Using Deep Learning for Defect Classification on a Small Weld X-ray Image Dataset

Ajmi, Chiraz; Zapata, Juan; Martínez-Álvarez, J. Javier; Domenech, G.; Ruíz, Ramón Eduardo

doi:10.1007/s10921-020-00719-9

Cited by 45 publications

(25 citation statements)

References 25 publications

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“…However, the results indicated that the PCA pre-processing led to nonlinear information losses, which had a deleterious influence on the training process. Furthermore, Ajmi et al [36] implemented data augmentation techniques, simultaneously replacing the channel substitution with a Canny edge image and an Adaptive Gaussian thresholding image on the Weld X-ray image dataset. This method seems to provide a great improvement for providing more specific information to the training model.…”

Section: Deep-learning-based Methods For Light Defect Datasetmentioning

confidence: 99%

Design of Multi-Receptive Field Fusion-Based Network for Surface Defect Inspection on Hot-Rolled Steel Strip Using Lightweight Dataset

et al. 2021

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With the advancement of industrial intelligence, defect recognition has become an indispensable part of facilitating surface quality in the steel manufacturing process. To assure product quality, most previous studies were typically trained with many defect samples. Nonetheless, a large quantity of defect samples is difficult to obtain, owing to the rare occurrence of defects. In general, deep learning-based methods underperformed as they have inherent limitations due to inadequate information, thereby restraining the application of models. In this study, a two-level Gaussian pyramid is applied to decompose raw data into different resolution levels simultaneously filtering the noises to acquire compact and representative features. Subsequently, a multi-receptive field fusion-based network (MRFFN) is developed to learn the hierarchical features and synthesize the respective prediction scores to form the final recognition result. As a result, the proposed method is capable of exhibiting an outstanding performance of 99.75% when trained using a lightweight dataset. In addition, the experiments conducted using the disturbance defect dataset showed the robustness of the proposed MRFFN against common noises and motion blur.

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Section: Deep-learning-based Methods For Light Defect Datasetmentioning

confidence: 99%

Design of Multi-Receptive Field Fusion-Based Network for Surface Defect Inspection on Hot-Rolled Steel Strip Using Lightweight Dataset

et al. 2021

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“…In the process of capturing the target from different angles, the internal parameters of the camera are regarded as constant, and the external parameters are different from each shooting angle. e number of optimized parameters increases significantly with the increase of the target image [7][8][9]. Rodrigues rotation equation provides a method of using vector to represent rotation.…”

Section: Camera Calibrationmentioning

confidence: 99%

A Sports Training Video Classification Model Based on Deep Learning

2021

Scientific Programming

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A sports training video classification model based on deep learning is studied for targeting low classification accuracy caused by the randomness of objective movement in sports training video. The camera calibration technology is used to restore the position of the target in the real three-dimensional space. After the camera calibration in the video, the sports training video is preprocessed. The input video segment is divided into equal length segments to obtain the subvideo segment. The motion vector field, brightness feature, color feature, and texture feature of the subvideo segment are extracted, and the extracted features are input into the AlexNet convolutional neural network. ReLU is used as the activation function in this convolutional neural network. Local response normalization is used to suppress and enhance the output of neurons to highlight the performance of useful information, so that the output classification results are more accurate. Event matching method is used to match the convolutional neural network output to complete the sports training video classification. The experimental results of the proposed study show that the model can effectively solve the problems of target moving randomness. The classification accuracy of sports training video is more than 99%, and the classification speed is faster which is shown from the results of the experiments.

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“…Shevchik et al (2020) proposed a method that could detect defects in process instability in real time based on a deep Artificial Neural Network (ANN); finally, the quality classification confidence was between 71% and 99%, revealing excellent application values [18]. Ajmi et al (2020) provided a comparative evaluation method of deep learning network performance for different combinations of parameters and hyperparameters and added an enhanced learning method to the dataset, which increased the model accuracy by approximately 3% [19]. Ajmi et al (2020) also applied Machine Learning (ML) and image processing tools to traditional crack detection and proposed a novel classification method based on deep learning networks using data enhancement for random image transformation on the data; it turned out that the model had the best performance in a short time [20].…”

Section: Introductionmentioning

confidence: 99%

Industrial Laser Welding Defect Detection and Image Defect Recognition Based on Deep Learning Model Developed

Deng

Feng

et al. 2021

Symmetry

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Aiming at the problem of the poor robustness of existing methods to deal with diverse industrial weld image data, we collected a series of asymmetric laser weld images in the largest laser equipment workshop in Asia, and studied these data based on an industrial image processing algorithm and deep learning algorithm. The median filter was used to remove the noises in weld images. The image enhancement technique was adopted to increase the image contrast in different areas. The deep convolutional neural network (CNN) was employed for feature extraction; the activation function and the adaptive pooling approach were improved. Transfer Learning (TL) was introduced for defect detection and image classification on the dataset. Finally, a deep learning-based model was constructed for weld defect detection and image recognition. Specific instance datasets verified the model’s performance. The results demonstrate that this model can accurately identify weld defects and eliminate the complexity of manually extracting features, reaching a recognition accuracy of 98.75%. Hence, the reliability and automation of detection and recognition are improved significantly. The research results can provide a theoretical and practical reference for the defect detection of sheet metal laser welding and the development of the industrial laser manufacturing industry.

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Using Deep Learning for Defect Classification on a Small Weld X-ray Image Dataset

Cited by 45 publications

References 25 publications

Design of Multi-Receptive Field Fusion-Based Network for Surface Defect Inspection on Hot-Rolled Steel Strip Using Lightweight Dataset

Design of Multi-Receptive Field Fusion-Based Network for Surface Defect Inspection on Hot-Rolled Steel Strip Using Lightweight Dataset

A Sports Training Video Classification Model Based on Deep Learning

Industrial Laser Welding Defect Detection and Image Defect Recognition Based on Deep Learning Model Developed

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