基于双向跨尺度融合深度网络的钢材表面缺陷检测

IF 1.1 4区 物理与天体物理 Q4 OPTICS
Zhihua Xie, Liang Jin
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引用次数: 0

摘要

在钢铁材料的工业生产中,由于环境和其他环境因素的影响,钢铁表面可能会出现各种复杂的缺陷。这些缺陷通常伴随着大量的背景纹理信息。特别是一些分辨率低、尺寸小的缺陷,容易出现误报和漏检。针对这些特定缺陷的问题,本文提出了一种结合无跨距卷积的双向跨尺度特征融合网络用于钢材表面缺陷检测。首先,为了提高模型的推理速度和减少参数的数量,在特征提取模块中引入了FasterNet的核心组件——简单而有效的卷积(convolution, PConv)来代替传统的ResNet算子。其次,嵌入双向交叉(BiC)模块,构建双向跨尺度特征融合网络(BiCCFM),提供更准确的定位线索,增强小目标上的特征表征;最后,结合非跨行卷积,开发SPD-Conv模块,对低分辨率图像中小目标的检测性能进行汇总。在nue - det公共数据集上的综合实验结果验证了嵌入式模块和所提模型的有效性。与其他最先进的方法相比,所提出的模型在保持相对较少的参数数量的情况下达到了最佳精度(74.2% mAP @ 0.5)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Steel surface defect detection based on bidirectional cross-scale fusion deep network

In the industrial production of steel materials, various complex defects may appear on the steel surface owing to the influence of environmental and other ambient factors. These defects are often accompanied by large amounts of background texture information. Especially, some defects with the low resolution and small size are prone to false alarms and missing detections. Aiming to address the issues of these specific defects, this paper proposes a bidirectional cross-scale feature fusion network combined with non-stridden convolution for steel surface defect detection. First, to improve the model’s inference speed and reduce the number of parameters, a simple yet effective convolution (PConv), the core component of FasterNet, is introduced in the feature extraction module instead of the traditional ResNet operator. Second, the bidirectional crossing (BiC) module is embedded to construct a bidirectional cross-scale feature fusion network (BiCCFM), which provides more accurate localization clues to enhance the feature representation on small targets. Finally, combined with non-stridden convolution, the SPD-Conv module is developed to aggregate the detection performance of small targets in low-resolution images. Comprehensive experimental results on the public NEU-DET dataset validate the effectiveness of the embedded modules and the proposed model. Compared with other state-of-the-art methods, the proposed model achieves the best accuracy (74.2% mAP @ 0.5) while maintaining a relatively small number of parameters.

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来源期刊
Optical Review
Optical Review 物理-光学
CiteScore
2.30
自引率
0.00%
发文量
62
审稿时长
2 months
期刊介绍: Optical Review is an international journal published by the Optical Society of Japan. The scope of the journal is: General and physical optics; Quantum optics and spectroscopy; Information optics; Photonics and optoelectronics; Biomedical photonics and biological optics; Lasers; Nonlinear optics; Optical systems and technologies; Optical materials and manufacturing technologies; Vision; Infrared and short wavelength optics; Cross-disciplinary areas such as environmental, energy, food, agriculture and space technologies; Other optical methods and applications.
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