An Extending Interclass Distance Real-Time Network Using Positional Orientation Transformation for Few-Shot Strip Steel Surface Defect Classification

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
He Zhang;Han Liu;Runyuan Guo;Qing Liu;Lili Liang;Wenlu Ma;Ding Liu
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引用次数: 0

Abstract

In the era of intelligent manufacturing, the rapid and accurate classification of strip steel surface defects is crucial. Deep learning typically relies on a large number of parameters and labeled samples to achieve outstanding performance. However, acquiring a sufficient number of defects in actual steel production poses challenges, and a high number of parameters can impact the real-time performance of defect classification. To tackle these issues, an extending interclass distance (Eid) real-time network using positional orientation transformation for few-shot strip steel surface defect classification is proposed (called the EidNet). EidNet utilizes a fewer parameters neural network as the feature extractor, enabling quick model convergence. To overcome the potential limitations of the fewer parameters model in expressing features, EidNet employs a no learnable parametric technique to artificially extend the interclass distance in the metric space, utilizing directional transformation of prototype positions to manually design the direction of extending, dispersing prototypes that are different from the query sample class as much as possible, thereby enhancing classification performance. The model uses the Euclidean distance as its classifier to maintain a low overall number of parameters. Experimental results demonstrate that EidNet significantly enhances the real-time performance of defect classification, striking a balance between real-time requirement and classification accuracy, while also exhibiting superior generalization capabilities.
基于位置取向变换的扩展类间距离实时网络用于少丸带钢表面缺陷分类
在智能制造时代,快速准确地对带钢表面缺陷进行分类至关重要。深度学习通常依赖于大量的参数和标记样本来实现出色的性能。然而,在实际的钢铁生产中获取足够数量的缺陷是一项挑战,大量的参数会影响缺陷分类的实时性。为了解决这些问题,提出了一种基于位置取向变换的扩展类间距离(Eid)实时网络,用于小丸带钢表面缺陷分类。EidNet利用较少参数的神经网络作为特征提取器,实现了快速的模型收敛。为了克服少参数模型在表达特征方面的潜在局限性,EidNet采用不可学习参数技术,在度量空间中人为地扩展类间距离,利用原型位置的方向性变换,手动设计扩展的方向,尽可能分散与查询样本类不同的原型,从而提高分类性能。该模型使用欧几里得距离作为分类器,以保持较低的总体参数数。实验结果表明,EidNet显著提高了缺陷分类的实时性,在实时性和分类精度之间取得了平衡,同时也表现出了优异的泛化能力。
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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