肖鹏程, 徐文广, 张妍, 朱立光, 朱荣, 许云峰. 基于SE注意力机制的废钢分类评级方法[J]. 工程科学学报, 2023, 45(8): 1342-1352. DOI: 10.13374/j.issn2095-9389.2022.06.10.002
引用本文: 肖鹏程, 徐文广, 张妍, 朱立光, 朱荣, 许云峰. 基于SE注意力机制的废钢分类评级方法[J]. 工程科学学报, 2023, 45(8): 1342-1352. DOI: 10.13374/j.issn2095-9389.2022.06.10.002
XIAO Peng-cheng, XU Wen-guang, ZHANG Yan, ZHU Li-guang, ZHU Rong, XU Yun-feng. Research on scrap classification and rating method based on SE attention mechanism[J]. Chinese Journal of Engineering, 2023, 45(8): 1342-1352. DOI: 10.13374/j.issn2095-9389.2022.06.10.002
Citation: XIAO Peng-cheng, XU Wen-guang, ZHANG Yan, ZHU Li-guang, ZHU Rong, XU Yun-feng. Research on scrap classification and rating method based on SE attention mechanism[J]. Chinese Journal of Engineering, 2023, 45(8): 1342-1352. DOI: 10.13374/j.issn2095-9389.2022.06.10.002

基于SE注意力机制的废钢分类评级方法

Research on scrap classification and rating method based on SE attention mechanism

  • 摘要: 为了解决传统人工方法对废钢分类评级人为因素干扰大且效率低下等问题,提出基于挤压−激励(Squeeze−Excitation,SE)注意力机制构建废钢分类评级的深度学习网络模型,并对采集到的废钢卸载过程图像进行模型训练和验证。首先,搭建物理尺寸比例为1∶3废钢质量查验物理模型,采用高分辨率视觉传感器模拟采集货车卸载废钢作业场景下不同废钢的形貌特征;然后,对采集到的废钢图像使用跨阶段局部网络进行特征提取,利用空间金字塔结构解决特征丢失问题,采用注意力机制关注通道间的相关性;最后,在包含7个标签分类的两个数据集进行模型训练与验证。实验表明:该模型能够有效地对不同级别的废钢进行自动评级判定,全类别准确率达到83.7%,全类别平均精度为88.8%,在准确性方面相比于传统人工验质方法具有显著优势,解决了废钢入库过程中质量评价的公正性难题。

     

    Abstract: Not only is scrap steel an indispensable ferritic raw material for the modern steel industry, but it is also the only green raw material that can replace iron ore in large quantities. The quality of the scrap steel directly affects the quality of molten steel, which makes it necessary to sort and grade scrap steel before it enters the furnace. Most iron and steel enterprises determine the grade of scrap steel mainly by visual inspection and caliper-based measurements by quality management personnel. As a result, this process is prone to human errors and low efficiency. Therefore, given that the major challenges of scrap inspection include the many categories of scrap, complex actual detection scenarios, and challenges in manual system connection, a deep learning network model CSSNet was proposed for scrap classification and rating based on the Squeeze-Excitation (SE) attention mechanism, and images of the scrap unloading process were collected for model training and validation. First, a 1∶3 physical model of scrap steel quality inspection was built to simulate this process. High-resolution visual sensors were used to collect images of diverse types of scrap steel in the scene of trucks unloading scrap steel. Then, a cross-stage local network was used to extract the features of the collected scrap images, the spatial pyramid structure was used to solve the problem of feature loss, and the attention mechanism was used to focus on the correlation between channels and retain the channel with the most feature information. Finally, model training and validation were done using two datasets containing seven labels for classification. In the model prediction stage, the constructed scrap steel quality inspection model CSSNet was used to judge the scrap steel category and quality to verify the accuracy and detection efficiency of the model. Based on the self-made scrap steel validation dataset, its performance was compared with mainstream single-stage object detection packages such as YOLOv4, YOLOv5s, and the two-stage object detection model Faster R-CNN. The model was found to be able to effectively rate different grades of scrap steel, with the classification accuracy rate of all categories has reached 83.7% and an mAP value of 88.8%. The performance of the CSSNet model is better than the other three target detection models. CSSNet can not only fully meet the needs of the actual production applications in terms of accuracy, real-time performance, and identification and rating efficiency but also surpass the traditional manual scrap quality inspection method, address multiple issues in the evaluation of scrap steel quality, and realize automated scrap steel quality testing.

     

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