Few-shot steel surface defect detection
WebNov 3, 2024 · Steel is an important raw material of fluid components. The technological level limitation leads to the surface faults of the steel, thus the key to improving fluid components quality is to diagnose the faults in steel production. The complex shape and small size of steel surface faults result in the low accuracy of the diagnosis, and the large size of the … WebFeb 1, 2024 · In addition, we construct a large-scale strip steel surface defects few shot classification dataset (FSC-20) with 20 different types. Experimental results show that the proposed method achieves the best performance compared to state-of-the-art methods for the 5-way 1-shot and 5-way 5-shot tasks. ... Surface defect detection of strip steel is ...
Few-shot steel surface defect detection
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WebNov 22, 2024 · Meanwhile, we release the first publicly available few-shot defect detection dataset, namely few-shot NEU-DET (FS-ND). This dataset will serve as a fair benchmark for various contrasting methods. Afterward, we analyze the characteristics of steel … IEEE websites place cookies on your device to give you the best user experience. By … WebThis paper presents a segmentation-based deep-learning architecture that is designed for the detection and segmentation of surface anomalies and is demonstrated on a specific domain of surface-crack detection.
WebJan 31, 2024 · 3. Conclusion. The surface defects of steel are taken as the research object in this paper. A new defect detection algorithm based on a deformable network combined with multiscale feature fusion algorithm is proposed in this paper in order to solve the problem of small size and complex shape of steel defect. WebClassification of steel surface defects in steel making industry is essential for the detection of defects through the classification of defects and for the analysis of causes …
Webto other existing few-shot learning methods for surface defects classification of hot-rolled steel strip. KEY WORDS: hot rolled strip; surface defect; few-shot learning; defect classification. a maximum pooling CNN for surface defects detection of hot rolled strip, and obtained an accuracy of 98.57% with a recognition speed of 0.008s. WebMar 10, 2024 · Few-shot defect recognition of metal surfaces via attention-embedding and self-supervised learning ... A steel surface defect inspection approach towards smart industrial monitoring ... This work focuses on applying advanced object detection techniques to surface defect inspection algorithm for sheet steel by applying …
WebJun 16, 2024 · In the field of wind turbine surface defect detection, most existing defect detection algorithms have a single solution with poor generalization to the dilemma of …
WebApr 1, 2024 · Detecting the surface defects in a lithium battery with an aluminium/steel shell is a difficult task. The effect of reflectivity, the limitation of acquiring the 3D information, and the shortage of massive amounts of labelled training data make the 2D detection method hard to classify surface defects. ilife h55WebNov 22, 2024 · To tackle this issue, we propose the first few-shot defect detection framework. Through pre-training models using data relevant to the target task, the … ilifehisWebThis data augmentation method can generate images outside the sampled data space along feature directions. •. Feature direction vector module is to find diversity-related feature directions and diversity weights. •. Range loss function is to limit the range of sampled data and then generate out-of-range images to improve the diversity. •. ilifehis-infolife technologiesWebSep 26, 2024 · In order to achieve the balance between accuracy and speed, Shi X et al. [13] proposed an improved network based on Faster R-CNN for the detection of steel surface defects. Tian R et al. [14] used ... ilife healthWebSep 13, 2024 · Besides, our network achieves 99% detection rate with speed of 83 FPS, which provides methodological support for real-time surface defects detection of steel strip. It can also predict the location and size information of defect regions, which is of great significance for evaluating the quality of an entire steel strip production line. ilife infosoftWebJun 26, 2024 · Surface defect detection plays a significant role in quality enhancement in the steel manufacturing industry. However, manual inspection of end products slows the entire manufacturing process and suffers from key shortcomings: (1) manual defect detection is time consuming and expensive, (2) the experience of the inspector is … ilife hifiWebQuality inspection is inevitable in the steel industry so there are already benchmark datasets for the visual inspection of steel surface defects. In our work, we show, contrary to previous recent articles, that a generic state-of-art deep neural network is capable of almost-perfect classification of defects of two popular benchmark datasets. ilife helpa filters