Automatic recognition of surface defects of hot rolled strip steel based on deep parallel attention convolution neural network

Zhao, YuFeng; Sun, XiaoLei; Yang, JiaXing

doi:10.1016/j.matlet.2023.135313

Cited by 5 publications

(1 citation statement)

References 11 publications

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“…In order to promote the development of defect detection, researchers have put forward many effective target detection methods [6]. Some of these methods use special subnetworks to provide candidate target location suggestions to improve the detection precision of the network, and some generate higher resolution super-resolution images for small target detection through subnetworks.…”

Section: Related Workmentioning

confidence: 99%

Improved Deep Learning Model for Workpieces of Rectangular Pipeline Surface Defect Detection

Chen,

Azman

2024

Computers

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This study introduces a novel approach to address challenges in workpiece surface defect identification. It presents an enhanced Single Shot MultiBox Detector model, incorporating attention mechanisms and multi-feature fusion. The research methodology involves carefully curating a dataset from authentic on-site factory production, enabling the training of a model with robust real-world generalization. Leveraging the Single Shot MultiBox Detector model lead to improvements integrating channel and spatial attention mechanisms in the feature extraction network. Diverse feature extraction methods enhance the network’s focus on crucial information, improving its defect detection efficacy. The proposed model achieves a significant Mean Average Precision (mAP) improvement, reaching 99.98% precision, a substantial 3% advancement over existing methodologies. Notably, the proposed model exhibits a tendency for the values of the P-R curves in object detection for each category to approach 1, which allows a better balance between the requirements of real-time detection and precision. Within the threshold range of 0.2 to 1, the model maintains a stable level of precision, consistently remaining between 0.99 and 1. In addition, the average running speed is 2 fps lower compared to other models, and the reduction in detection speed after the model improvement is kept within 1%. The experimental results indicate that the model excels in pixel-level defect identification, which is crucial for precise defect localization. Empirical experiments validate the algorithm’s superior performance. This research represents a pivotal advancement in workpiece surface defect identification, combining technological innovation with practical efficacy.

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Section: Related Workmentioning

confidence: 99%