A Transfer Residual Neural Network Based on ResNet-50 for Detection of Steel Surface Defects
Source abstract
Abstract
With the increasing popularity of deep learning, enterprises are replacing traditional inefficient and non-robust defect detection methods with intelligent recognition technology. This paper utilizes TL (transfer learning) to enhance the model’s recognition performance by integrating the Adam optimizer and a learning rate decay strategy. By comparing the TL-ResNet50 model with other classic CNN models (ResNet50, VGG19, and AlexNet), the superiority of the model used in this paper was fully demonstrated. To address the current lack of understanding regarding the internal mechanisms of CNN models, we employed an interpretable algorithm to analyze pre-trained models and visualize the learned semantic features of defects across various models. This further confirms the efficacy and reliability of CNN models in accurately recognizing different types of defects. Results showed that the TL-ResNet50 model achieved an overall accuracy of 99.4% on the testing set and demonstrated good identification ability for defect features.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Non-Destructive Testing Techniques · Integrated Circuits and Semiconductor Failure Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Petroleum Engineering, Northeast Petroleum University, Daqing 163318, China
School of Mechanical Science and Engineering, Northeast Petroleum University, Daqing 163318, China
Funding
Training Plan of Young Innovative Talents in Heilongjiang Province Ordinary Undergraduate Universities (UNPY) · Natural Science Foundation of Heilongjiang Province · Postdoctoral Research Foundation of Heilongjiang Province
References (30)
Show source-provided references
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