Image-Based Surface Defect Detection Using Deep Learning: A Review

DCC-CenterNet: A rapid detection method for steel surface defects

Over the last few decades, detecting surface defects has attracted significant attention as a challenging task. There are specific classes of problems that can be solved using traditional image processing techniques. However, these techniques struggle with complex textures in backgrounds, noise, and differences in lighting conditions. As a solution to this problem, deep learning has recently emerged, motivated by two main factors: accessibility to computing power and the rapid digitization of society, which enables the creation of large databases of labeled samples. This review paper aims to briefly summarize and analyze the current state of research on detecting defects using machine learning methods. First, deep learning-based detection of surface defects on industrial products is discussed from three perspectives: supervised, semi-supervised, and unsupervised. Secondly, the current research status of deep learning defect detection methods for X-ray images is discussed. Finally, we summarize the most common challenges and their potential solutions in surface defect detection, such as unbalanced sample identification, limited sample size, and real-time processing.

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Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review

Strip steel is an indispensable material in the manufacturing industry and the defects of the surface directly determine the quality. Due to the diversity and complexity of surface defects in intraclass and between interclass, a great deal of manpower and resources have been devoted to surface defect detection. This article proposes a new deep learning detection network, channel attention, and bidirectional feature fusion on a fully convolutional one-stage (CABF-FCOS) network to achieve rapid and effective defect detection on steel strips. First, the anchor-free FCOS is proposed as the detection framework to eliminate affections of those hyperparameters related to the anchor. Second, a channel attention mechanism (CAM) module is proposed to reduce the loss of feature information. Finally, it replaces the feature pyramid network (FPN) with a bidirectional feature fusion network (BFFN) for more effective feature fusion on the images. This reduces the loss of feature information and performs feature fusion to improve the defect detection performance. The experiments show that the mean average precision (mAP) is 76.68% at a detection speed of 18 frames per second (FPS) and improved by 4.43% over FCOS, which is higher than the state-of-the-art (SOTA) detection methods. This indicates that CABF-FCOS can obtain satisfactory defect detection performance and meet the demand of real-time detection in the industry.

Surface Defect Detection of Steel Strips Based on Anchor-Free Network With Channel Attention and Bidirectional Feature Fusion

Center-based Transfer Feature Learning With Classifier Adaptation for surface defect recognition

Surface quality assessment of magnetic tile before mounting is extremely significant. At present, this task is mainly accomplished by experienced workers in industry, which exposes the drawbacks of low efficiency and high cost. To overcome these issues, an intelligent system is developed to perform this task, which appears to be an efficient and reliable substitute for human workers. In this article, deep learning technique is embedded into our system for automatic defect identification. However, conventional convolutional neural network (CNN) is not suitable for this classification task, since the input is a sample rather than a single image. To overcome this problem, an end-to-end CNN architecture is proposed, termed fusion feature CNN (FFCNN). FFCNN consists of three modules: feature extraction module, feature fusion module, and decision-making module. The feature extraction module is designed to extract features from different images. The feature fusion module is to fuse the features extracted by feature extraction module. The decision-making module is to predict the label by the fused features. Furthermore, an attention mechanism is introduced to focus on more representative parts and suppress less important information. Experimental results demonstrated that the developed system is effective and efficient for magnetic tile surface defect detection.

FFCNN: A Deep Neural Network for Surface Defect Detection of Magnetic Tile

The invention discloses a laser metal additive manufacturing fusion depth prediction system based on deep learning and transfer learning, which comprises a printing workbench, an image acquisition device, a thermal imager, a man-machine interaction device, a display and a host; the image acquisition device, the thermal imager, the man-machine interaction device and the display are electrically connected with the host. Molten pool images and temperature images are continuously collected in a certain time sequence; firstly, normalization processing is carried out on an effective molten pool image and an effective temperature image; the parameters of the picture size and the pixel size of the molten pool image are kept consistent, other irrelevant features are eliminated during training of the deep learning convolutional neural network model, only key features are trained, and the system has the advantage that the training efficiency of the deep learning convolutional neural network modelis improved; and the fusion depth is predicted by adopting a deep learning convolutional neural network model, so that the precision of parameters can be effectively improved.

Metal additive forming fusion depth real-time prediction method based on depth and transfer learning

The invention discloses an additive manufacturing forming size prediction system based on deep learning and feature fusion, which comprises a printing workbench, an image acquisition device, a thermalimager, a man-machine interaction device, a display and a host. The image acquisition device, the thermal imager, the man-machine interaction device and the display are electrically connected with the host. Molten pool images and temperature images are continuously collected in a certain time sequence; firstly, normalization processing is carried out on an effective molten pool image and an effective temperature image; the parameters of the picture size and the pixel size of the molten pool image are kept consistent, other irrelevant features are eliminated during training of the deep learning convolutional neural network model, only key features are trained, and the method has the advantage that the training efficiency of the deep learning convolutional neural network model is improved;and the deep learning convolutional neural network model is adopted to predict the single-channel forming height, so that the precision of parameters can be effectively improved.

Metal additive forming size real-time prediction method based on depth feature fusion

The invention discloses a laser metal additive manufacturing forming precision prediction system based on deep learning, which comprises a printing workbench, an image acquisition device, a man-machine interaction device, a display and a host. The image acquisition device, the man-machine interaction device and the display are electrically connected with the host. Molten pool images and temperature images are continuously collected in a certain time sequence; firstly, normalization processing is carried out on an effective molten pool image and an effective temperature image; the parameters ofthe picture size and the pixel size of the molten pool image are kept consistent, other irrelevant features are eliminated during training of the deep learning convolutional neural network model, only key features are trained, and the method has the advantage that the training efficiency of the deep learning convolutional neural network model is improved; and the deep learning convolutional neural network model is adopted to predict the single-channel forming width, so that the precision of parameters can be effectively improved.

Metal additive manufacturing forming size real-time prediction method based on deep learning

The invention discloses a laser metal additive deposition fusion state real-time prediction method and system, and the method comprises the following steps: (1), building a real-time prediction initial model, the real-time prediction initial model comprising a backGAN network used for converting a process parameter data set into image data, and a plurality of paths of parallel convolutional neuralnetworks connected with the backGAN network and used for extracting feature data in the image data, a feature fusion layer connected with the multiple paths of parallel convolutional neural networksand used for carrying out feature fusion on the feature data output by the multiple paths of convolutional neural networks, a full connection layer and a classification layer sequentially connected behind the feature fusion layer; (2), training the real-time prediction initial model to form a real-time prediction final model; and (3), inputting a process parameter data set into the real-time prediction final model to predict a laser metal additive deposition fusion state in real time. By constructing the prediction model of the backGAN network and the VGG19 network, the molten pool state can be predicted through process parameters, a neural network with supervised learning is formed, and the cost is low.

Laser metal additive deposition fusion state real-time prediction method and system

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