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In steel production, defect detection is crucial for preventing safety risks, and improving the accuracy of steel defect detection in industrial environments remains challenging due to the variable types of defects, cluttered backgrounds, low contrast, and noise interference. Therefore, this paper introduces a steel surface defect detection model, DBCW-YOLO, based on YOLOv5. Firstly, a new feature fusion strategy is proposed to optimize the feature map fusion pair model using the BiFPN method to fuse information at multiple scales, and CARAFE up-sampling is introduced to expand the sensory field of the network and make more effective use of the surrounding information. Secondly, the WIoU uses a dynamic non-monotonic focusing mechanism introduced in the loss function part to optimize the loss function and solve the problem of accuracy degradation due to sample inhomogeneity. This approach improves the learning ability of small target steel defects and accelerates network convergence. Finally, we use the dynamic heads in the network prediction phase. This improves the scale-aware, spatial-aware, and task-aware performance of the algorithm. Experimental results on the NEU-DET dataset show that the average detection accuracy is 81.1, which is about (YOLOv5) 6% higher than the original model and satisfies real-time detection. Therefore, DBCW-YOLO has good overall performance in the steel surface defect detection task. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence technology and the popularity of intelligent production projects, intelligent inspection systems have gradually become a hot topic in the industrial field. As a fundamental problem in the field of computer vision, how to achieve object detection in the industry while taking into account the accuracy and real-time detection is an important challenge in the development of intelligent detection systems. The detection of defects on steel surfaces is an important application of object detection in the industry. Correct and fast detection of surface defects can greatly improve productivity and product quality. To this end, this paper introduces the MSFT-YOLO model, which is improved based on the one-stage detector. The MSFT-YOLO model is proposed for the industrial scenario in which the image background interference is great, the defect category is easily confused, the defect scale changes a great deal, and the detection results of small defects are poor. By adding the TRANS module, which is designed based on Transformer, to the backbone and detection headers, the features can be combined with global information. The fusion of features at different scales by combining multi-scale feature fusion structures enhances the dynamic adjustment of the detector to objects at different scales. To further improve the performance of MSFT-YOLO, we also introduce plenty of effective strategies, such as data augmentation and multi-step training methods. The test results on the NEU-DET dataset show that MSPF-YOLO can achieve real-time detection, and the average detection accuracy of MSFT-YOLO is 75.2, improving about 7% compared to the baseline model (YOLOv5) and 18% compared to Faster R-CNN, which is advantageous and inspiring. [ABSTRACT FROM AUTHOR]

With the advance in Industry 4.0, smart industrial monitoring has been proposed to timely discover faults and defects in industrial processes. Steel is widely used in manufacturing equipment, and steel surface defect inspection is of great significance to the normal operation of steel equipment in manufacturing workshops. In steel defect inspection systems, industrial inspection robots generate images via scanning steel surface, and processors perform surface defect inspection algorithms on images. We focus on applying advanced object detection techniques to surface defect inspection algorithm for sheet steel. In the proposed steel surface defect inspection model, a deformable convolution enhanced backbone network firstly extracts complex features from multi-shape steel surface defects. Then the feature fusion network with balanced feature pyramid generates high-quality multi-resolution feature maps for the inspection of multi-size defects. Finally, detector network achieves the localization and classification of steel surface defects. The proposed model is evaluated on a typical steel surface defect dataset. Our model achieves 0.805 mAP, 0.144 higher than baseline models, and our model shows high efficiency in inference. Experiments are performed to reveal the effect of employed approaches, and results also show our model achieves a balance between inspection performance and inference efficiency. [ABSTRACT FROM AUTHOR]

Inspecting steel surfaces is important to ensure steel quality. Numerous defect-detection methods have been developed for steel surfaces. However, they are primarily used for local defects, and their accuracy in detecting distributed defects is unsatisfactory because such defects are difficult to locate and have complex texture characteristics. To solve these issues, an improved random forest algorithm with optimal multi-feature-set fusion (OMFF-RF algorithm) is proposed for distributed defect recognition in this paper. The OMFF-RF algorithm includes the following three aspects. First, a histogram of oriented gradient (HOG) feature-set and a gray-level co-occurrence matrix (GLCM) feature-set are extracted and fused to describe local and global texture characteristics, respectively. Second, given the small number of samples of distributed defect images and the high dimensionality of the extracted feature-sets, a random forest algorithm is introduced to perform defect classification. Third, the feature-sets vary greatly in performance and dimensionality. To improve the fusion efficiency, OMFF-RF merges the HOG feature-set and the GLCM feature-set through a multi-feature-set fusion factor, which changes the number of decision trees that correspond to each feature-set in the RF algorithm. The OMFF factor is found by optimizing the fitting curve of the classification accuracy of the test set using a stepping multi-feature-set fusion factor. In experiments, the effectiveness of the proposed OMFF-RF was verified using 5 types of distributed defects collected from an actual steel production line. OMFF-RF achieved a recognition accuracy of 91%, a result superior to support vector machine (SVM) and conventional RF algorithms. [ABSTRACT FROM AUTHOR]

For steel surface defect recognition, feature extraction and classification are very important steps. In this paper, multi-type statistical features and enhanced twin support vector machine classifier are formulated and applied. Firstly, four types of statistical features for different attributes of defect region are proposed. They are insensitive to affine transformation in scale and rotation. And those attributes include shape distance and local binary pattern operators with sign and magnitude. Then, dummy boundary samples and representative samples are extracted from steel surface defect dataset. Dummy boundary samples include the sparse boundary information of dataset. They can reduce the adverse impact of noise samples. Representative samples with local and global properties are used to replace samples with quadratic loss. They can exclude noise samples. Based on dummy boundary samples and representative samples, enhanced twin support vector machine is formulated. On one hand, it can solve multi-class classification problem. On the other hand, it has anti-noise ability and high classification efficiency. At last, enhanced twin support vector machine classifier and multi-type statistical features are applied to recognize five types of steel surface defects. The experimental results show that our proposed multi-class classifier has perfect performance in efficiency and accuracy. And multi-type statistical features are in favor of improving classification performance. [ABSTRACT FROM AUTHOR]

There are several types of steel products, such as wire rods, cold-rolled coils, hot-rolled coils, thick plates, and electrical sheets. Surface stains on cold-rolled coils are considered defects. However, surface stains on thick plates are not considered defects. A conventional optical structure is composed of a camera and lighting module. A defect inspection system that uses a dual lighting structure to distinguish uneven defects and color changes by surface noise is proposed. In addition, an image processing algorithm that can be used to detect defects is presented in this paper. The algorithm consists of a Gabor filter that detects the switching pattern and employs the binarization method to extract the shape of the defect. The optics module and detection algorithm optimized using a simulator were installed at a real plant, and the experimental results conducted on thick steel plate images obtained from the steel production line show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Computer vision is augmented in various manufacturing industries to perform automated inspection operations accurately and efficiently. It has been observed that the performance of vision-based inspection approaches degrades considerably upon utilizing images captured under shop-floor conditions. This work proposes utilizing Histogram Equalization and adversarial training through Neural Structure Learning (NSL) for developing a robust vision-based Surface Defect Classification framework. A novel deep neural network architecture obtains adversarial samples in the extracted feature space instead of obtaining the same in the original input image space. The architecture can be easily integrated and employed with various machine learning models. A commonly employed steel surface defect dataset (NEU) with practical relevance to industrial cases is selected for the model training and experimental studies. The robustness of the proposed approach is evaluated over the Extended Diversity Enhanced (ENEU) dataset derived by simulating image acquisition variations similar to shop floor conditions. The results reveal that the proposed approach enhances the recognition accuracy of the baseline method from 87.7% to 92.4% over ENEU. The prediction accuracy of the proposed approach is considerably better than the traditional methods and deep learning competitors over ENEU. The qualitative and quantitative comparison of results obtained using the present approach with methods reported in the literature demonstrates the effectiveness of adversarial training in improving the generalization abilities of machine learning models. [ABSTRACT FROM AUTHOR]