Department of Electrical Engineering, Yuan Ze University, Taoyuan 320, Taiwan.
School of Electrical Engineering, Telkom University, Bandung 40257, Indonesia.
Sensors (Basel). 2022 Jun 2;22(11):4257. doi: 10.3390/s22114257.
Defects are the primary problem affecting steel product quality in the steel industry. The specific challenges in developing detect defectors involve the vagueness and tiny size of defects. To solve these problems, we propose incorporating super-resolution technique, sequential feature pyramid network, and boundary localization. Initially, the ensemble of enhanced super-resolution generative adversarial networks (ESRGAN) was proposed for the preprocessing stage to generate a more detailed contour of the original steel image. Next, in the detector section, the latest state-of-the-art feature pyramid network, known as De-tectoRS, utilized the recursive feature pyramid network technique to extract deeper multi-scale steel features by learning the feedback from the sequential feature pyramid network. Finally, Side-Aware Boundary Localization was used to precisely generate the output prediction of the defect detectors. We named our approach EnsGAN-SDD. Extensive experimental studies showed that the proposed methods improved the defect detector's performance, which also surpassed the accuracy of state-of-the-art methods. Moreover, the proposed EnsGAN achieved better performance and effectiveness in processing time compared with the original ESRGAN. We believe our innovation could significantly contribute to improved production quality in the steel industry.
缺陷是影响钢铁行业产品质量的主要问题。开发检测缺陷的具体挑战在于缺陷的模糊性和微小尺寸。为了解决这些问题，我们提出了结合超分辨率技术、序列特征金字塔网络和边界定位的方法。首先，在预处理阶段提出了集成增强超分辨率生成对抗网络（ESRGAN），以生成原始钢图像更详细的轮廓。接下来，在检测器部分，使用最新的基于特征金字塔网络的检测算法（称为 De-tectoRS），通过学习序列特征金字塔网络的反馈，利用递归特征金字塔网络技术提取更深层次的多尺度钢特征。最后，使用侧感知边界定位精确生成缺陷检测器的输出预测。我们将这种方法命名为 EnsGAN-SDD。广泛的实验研究表明，所提出的方法提高了缺陷检测器的性能，其准确性也超过了最先进方法的准确性。此外，与原始 ESRGAN 相比，所提出的 EnsGAN 在处理时间方面表现出更好的性能和效率。我们相信我们的创新可以为钢铁行业提高生产质量做出重大贡献。
