Triplet-Graph Reasoning Network for Few-Shot Metal Generic Surface Defect Segmentation

Quality control in metal product manufacturing relies heavily on accurately detecting and classifying surface defects through visual inspection. Recently, convolutional neural networks (CNNs) have shown promising results in automating this process with high accuracy. This research paper proposes a new (experimental version) Lite Convolutional Neural Network (LCNN) designed to analyze image data to detect and classify surface defects on metallic surfaces. Our model was trained on a metal surface defects dataset comprising 1800 images of six different types of surface defects. Despite using relatively small datasets, the proposed LCNN version achieves a classification accuracy of 91.67%, highlighting its effectiveness in real-world defect detection scenarios.

Computer vision and deep learning techniques are the most emerging technologies in this era. Both of these can greatly raise the rate at which defects on metal surfaces are identified while performing industrial quality checks. The identification of faults over metal surfaces can be viewed as a significant challenge since they are easily impacted by ambient factors including illumination and light reflections. This paper proposes novel metal surface defect detection network called as YOLOv-5s-FRN in response to the problems of ineffective detection brought by the conventional manual inspection system. The proposed system is developed through the integration of a novel architectural module called as Feature Recalibration Network (FRN) to the You Only Look Once-version-5 small network )YOLOv-5s(. In order to extract the global feature information from the provided image, FRN is able to evaluate the interdependencies between the channels. This improves the feature discrimination capability and prediction accuracy of the defect detection system. The incorporation of FRN structure makes YOLOv-5s architecture to selectively enhance the necessary features and discard the unwanted ones. Therefore, the proposed novel method will efficiently detect and classify the metal surface defects such as crazing, patches, inclusions, scratches, pitted surfaces and rolled in scale. North Eastern University surface defect database (NEU-DET) has been used to train and test the proposed architectural model. The suggested system has been compared with alternative models based on several performance matrices such as precision, recall and Mean Average Precision (mAP). It is observed that the proposed YOLOv-5s-FRN architecture provides significant performance improvement than state-of-the-art methods. The proposed system has been provided satisfactory results by means of improvement in mAP and time consumption. The proposed model has delivered value of mAP_0.5 as 98.05% and that of mAP_0.5:0.95 as 89.03%.

Deep learning-based surface defect segmentation approaches have shown promising performance in recent years. However, segmenting defects with complex shapes, large variations in size, and weakly textured defects with indistinct characteristics still poses significant challenges. In this article, a novel semantic guidance and texture priors based dual-branch surface defect segmentation network (SGTP-Net) is proposed for those issues. Firstly, we construct a feature extraction network combines semantic and texture branches. The semantic branch establishes global contextual relationships, while the texture branch captures local features of defects, this dual-branch ensured the network to extract features from various complex defects. Secondly, we design a feature fusion strategy based on semantic guidance and texture priors. The semantic information is used to guides the output of texture branch. After that, the guided texture information provides valuable edge texture priors for each layers output in semantic branch. The two branches mutually guide each other for improving ability of weak textures feature extraction. Finally, we run our method on the NEU-Seg, MT-Defect and MSD datasets to conduct a comprehensive comparison with some state-of-the-art general object segmentation models and specialized surface defect segmentation methods. The experimental results show that our SGTP-Net performs well in surface defect detection, offering excellent semantic segmentation accuracy and exhibiting good stability and robustness in detecting various surface defects.

The quality of metal products plays a crucial role in determining their overall performance, reliability and safety. Therefore, timely and effective detection of metal surface defects is of great significance. For this purpose, we present a densely connected network with multi-attention for metal surface defect segmentation, called MADC-Net. Firstly, we selected ResNet50 as the encoder due to its robust performance. To capture richer contextual information from the defect feature map, we designed a densely connected network and incorporated the multi-attention of a CESConv module, an efficient channel attention module (ECAM), and a simple attention module (SimAM) into the decoder. In addition, in the final stage of the decoder, we introduced a reconfigurable efficient attention module (REAM) to reduce redundant calculations and enhance the detection of complex defect structures. Finally, a series of comprehensive comparative and ablation experiments were conducted on the publicly available SD-saliency-900 dataset and our self-constructed Bearing dataset, all of which validated that our proposed method was effective and reliable in defect segmentation. Specifically, the Dice and Jaccard scores for the SD-saliency-900 dataset were 88.82% and 79.96%. In comparison, for the Bearing dataset, the Dice score was 78.24% and the Jaccard score was 64.74%.

Automatic surface defect segmentation for hot-rolled steel strip using depth-wise separable U-shape network

The ambient stability, hysteresis, and trap states in organo-halide perovskite solar cells (PSCs) are correlated to the influence of interlayer interfaces and grain boundaries. Astute incorporation of Cu2ZnSnS4 (CZTS) and Au/CZTS core/shell nanocrystals (NCs) can realize the goal of simultaneously achieving better performance and ambient stability of the PSCs. With optimized Au/CZTS NC size and concentration in the photoactive layer, power conversion efficiency can be increased up to 19.97 ± 0.6% with ambient air stability >800 h, as compared to 14.46 ± 1.02% for the unmodified devices. Through efficient carrier generation by CZTS and perovskite, accompanied by the plasmonic effect of Au, carrier density is sufficiently increased as validated by transient absorption spectroscopy. NCs facilitate the interfacial charge transfer by suitable band alignment and removal of recombination centers such as metallic Pb0, surface defects, or impurity sites. NC embedding also increases the perovskite grain size and assists in pinhole filling, reducing the trap state density.

Pulsed Eddy Current (PEC) is a new emerging Non Destructive Evaluation technique for sub-surface defect detection. It provides new challenges to signal analysis and interpretation approach applied to the inspection evaluation. For instance, PEC could suffer from noise and be not sufficient to extract more information about the defects. This paper aims to approach the challenge of flaw identification in PECs. Due to non-Gaussianity of PEC measurements, we applied Independent Component Analysis (ICA) in extracting information from PEC responses. We considered three different approaches implementing ICA, in order to project the response signals of various defects into the Independent Components (ICs) feature space. Then, useful ICs of each algorithm were used as features for machine learning algorithms, in order to solve the inverse problem of pattern classification. Since the nongaussianity of the OEC measurements, we retained ICs with highest kurtosis. The considered different kinds of defects were: metal loss, sub-surface cracks, surface defects and slants. We compared the performances of our implemented algorithms with results available in scientific literature. We obtained improvements in reliability of the pattern classification algorithm, as well as in reducing the computational load, obtaining a classification error of 8.54% over 3063 testing patterns.

Characterization of surface defects after flanging of metallic sheets

The defect inspection in traditional metal processing usually adopts the manual visual inspection, which is easy to cause problems such as false detection and leakage. Besides, the detection rate of existing metal defect inspection has been affected by the high reflectivity of metal surface. For avoiding the problems of existing detection algorithms, a metal defect detection system based on laser triangulation and laser vision is given. The system is composed of industrial camera and strip light source. In order to avoid the interference of metal reflection on defect detection, the green strip light is used as the light source of the system. Based on the proposed system, this paper presents a metal surface detection algorithm with three primary color channels, that is, metal surface defects are identified by identifying the RGB values of metal surface pixels. Finally, through static experiments, the detection rate of the metal defect surface detection system based on laser vision is 100%. The defect detection method proposed in this paper can effectively avoid the influence of high reflectivity of metal surface on the detection rate, and can be widely used in the field of metal processing.

Using deep learning to identify the depth of metal surface defects with narrowband SAW signals

Accurate identification of defect morphology and size distribution on metallic surfaces is crucial for manufacturing, particularly in the steel industry where such materials are widely used. This study proposes a novel lightweight architecture based on YOLOv7-tiny to address the challenges of real-time metallic surface defect detection. The original anchor-based detection head is replaced with an anchor-free mechanism to reduce missed detections of defects with extreme aspect ratios, while a logarithmic transformation-based enhancement is introduced to strengthen defect features. The backbone is replaced by the lightweight MobileNetV3-large network to reduce the number of parameters, and an Efficient Multi-Scale Attention (EMA) module is embedded into the bottleneck structure to increase target importance and suppress background interference, especially for small defects. In addition, a bidirectional feature pyramid network with adaptively spatial feature fusion (BAFPN) is integrated as the Head architecture to provide richer semantic information. The improved model was evaluated on the DAGM 2007, NEU-DET, and GC10-DET datasets, which encompass metal surface defects with varying degrees of complexity. The results demonstrate an average increase of 6.24% in mean average precision (mAP) and an inference speed over 90 FPS, confirming both the efficacy and real-time capability of the proposed method.

Metal surface quality control is significant in the production line of metal products. Detecting metal surface defects is challenging due to the various types and morphological patterns. Recent advances have witnessed deep learning-based automated optical inspection systems as a promising solution. This paper presents an enhanced YOLOv4 model for metal surface defect detection (MSDD). Specifically, we integrate three boosting components into the original YOLOv4, including 1) a self-dependent attentive fusion (SAF) block, placed within the model neck, to enhance inter-path and cross-layer feature fusion, 2) a component randomized Mosaic augmentation (CRMA) scheme to strategically discourage an over-transformed image to participate in training, and 3) a perturbation agnostic (PA) label smoothing method to keep the model from making over-confident predictions and thus act as a means of regularization. The proposed method has been validated on a self-developed MSDD dataset. It is shown that each boosting component can lead to an impressive mAP gain, and the final model outperforms the baselines, namely, Faster R-CNN, YOLOv4, YOLOv5, and YOLOX, by 7.85%, 6.51%, 3.76%, and 3.57%, respectively.

Surface defect segmentation is the basis of industrial production, where pixel-level defect prediction plays a vital role in many applications. As a new trend, few-shot defect detection aims to recognize defects by combining information from a few defect samples. However, many traditional few-shot methods do not consider patch-level correlations between input images and memory banks. This work introduces a novel representation framework consisting hierarchical normal and defect memory banks for few-shot surface defect detection. We propose an adaptive feature bank updating scheme that dynamically integrates frequent features while discarding rare ones. We design a fine-grain feature replacement module rooted in a matching attention mechanism to highlight ambiguous regions for input images and use fused features to identify overall defects. On benchmark datasets, our strategy can obtain in-depth insights from multiple scales, which showcase superiority in generalization for unidentified anomalies better than others in experiments. The experimental results for the ten-shot scenarios demonstrate that the proposed method achieves an average pixel AUC of 88.6%, 93.7%, and 98.7% on three benchmark datasets, respectively, and with more genuine and pseudo anomalous samples, the proposed approach can achieve 98.5% and 95.5% in image AUC and pixel F1 score, respectively, outperforming the state-of-the-art with 1.2% and 24% in each metric.

Automatic detection of anomalies on the metal surface is an essential capability in industries to provide the better-quality control. To locate and identify the type of defect, it is necessary to find the Region of interest (RoI) from the captured image. Segmentation of the captured image is one among the many methods to achieve this task. Therefore, a precise and accurate segmentation method has major role to improve the metal surface anomaly detection rate in industry. As the defects are different in it’s size, shape and type, the process of semantic segmentation for metal surface is considered as a challenging task. To address this issue, a deep learning based high calibre U- shaped network is proposed. It can be considered as an automatic quality control system for industries. The proposed method is effective in predicting the presence of defects. The system is also capable to locate the position of the defect on surface without the intervention of human being. The up-sampling technique provided with the convolutional neural network in the architecture makes the system to produce high resolution outputs. The proposed system has been evaluated based on accuracy, precision, loss and IoU after training and testing the model using two different datasets called NEU metal surface defect database and Kolektor surface defect data set.

Gradation distribution of the C-scan image is uneven, the defect area and the background area are mixed, it is difficult to achieve quantitative analysis of test pieces. This paper proposes a method to segment the C-scan image of the surface defects of aluminum plate specimens by Otsu threshold method. Binary image containing contour features of defective areas can be segmented by this method, specific geometric parameter information of the defect area can be obtained by tracing the binary image. The validity of the proposed method was verified by quantitative analysis of this aluminum plate specimens. Meanwhile, the performance of different image segmentation methods are compared, it can be seen from the results of measurement errors that Otsu threshold method can accurately identify the contour of the defect area, and the segmentation of the defect area is complete.