Wang Yuanyuan, Yin Tongtong, Chen Xiuchuan, Hauwa Abdullahi Suleiman, Deng Boyang, Zhu Yemeng, Gao Shangbing, Zang Haiyan, Zhao Hu
College of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, 223003, China.
Laboratory for Internet of Things and Mobile Internet Technology of Jiangsu Province, Huaian, 223001, China.
Sci Rep. 2024 Nov 12;14(1):27694. doi: 10.1038/s41598-024-79205-5.
Scratches and cracks in steel severely affect its service life and performance. However, owing to the irregular shapes and sizes of steel surface defects, defects within the same class may be different, whereas defects between classes may be similar. Existing methods focus only on spatial information, resulting in low detection accuracy. To alleviate these problems, this paper proposes the ECDY (EIFEM CARAFE DyHead) network to enhance the detection capability of steel defects. We first design a feature extraction module that focuses on the edge information of feature contours. This module uses the Sobel operator to extract the edge information of a feature and fuses it with the overall spatial information so that richer semantic information can be obtained. The module has improved accuracy in the YOLOv5, YOLOv8, and YOLOv10 versions, and uses fewer parameters and calculations. In particular, in YOLOv8x, mAP@0.5 increased by 2.5%, and the number of parameters was reduced by 12.4 M. Second, to retain the detailed information in the feature pyramid, and to better reconstruct features, we choose the content-aware reassembly feature method (CARAFE) as the upsampling method. Finally, the detection head was replaced with a dynamic unified detection head (DyHead) to adapt to different defect sizes and different task requirements. Compared with YOLOv8s, the proposed method improves precision by 1.6%, recall by 4%, and mAP@0.5 by 4%. This value is 4.2% higher than the mAP@0.5 of the current SOTA model RT-DETR-L in the field of object detection and has 23.2 M fewer parameters.
钢材中的划痕和裂纹会严重影响其使用寿命和性能。然而，由于钢材表面缺陷的形状和尺寸不规则，同一类缺陷可能存在差异，而不同类缺陷可能相似。现有方法仅关注空间信息，导致检测精度较低。为缓解这些问题，本文提出了ECDY（EIFEM CARAFE DyHead）网络以增强钢材缺陷的检测能力。我们首先设计了一个专注于特征轮廓边缘信息的特征提取模块。该模块使用Sobel算子提取特征的边缘信息，并将其与整体空间信息融合，从而获得更丰富的语义信息。该模块在YOLOv5、YOLOv8和YOLOv10版本中提高了精度，且使用的参数和计算量更少。特别是在YOLOv8x中，mAP@0.5提高了2.5%，参数数量减少了1240万个。其次，为保留特征金字塔中的详细信息，并更好地重建特征，我们选择内容感知重组特征方法（CARAFE）作为上采样方法。最后，将检测头替换为动态统一检测头（DyHead），以适应不同的缺陷尺寸和不同的任务要求。与YOLOv8s相比，所提方法的精度提高了1.6%，召回率提高了4%，mAP@0.5提高了4%。该值比目标检测领域当前的SOTA模型RT-DETR-L的mAP@0.5高4.2%，且参数数量少2320万个。
