Robotic Intelligence and Automation ( IF 2.1 ) Pub Date : 2021-12-02 , DOI: 10.1108/aa-04-2021-0044 Jiawei Lian 1 , Junhong He 1 , Yun Niu 1 , Tianze Wang 1
Purpose
The current popular image processing technologies based on convolutional neural network have the characteristics of large computation, high storage cost and low accuracy for tiny defect detection, which is contrary to the high real-time and accuracy, limited computing resources and storage required by industrial applications. Therefore, an improved YOLOv4 named as YOLOv4-Defect is proposed aim to solve the above problems.
Design/methodology/approach
On the one hand, this study performs multi-dimensional compression processing on the feature extraction network of YOLOv4 to simplify the model and improve the feature extraction ability of the model through knowledge distillation. On the other hand, a prediction scale with more detailed receptive field is added to optimize the model structure, which can improve the detection performance for tiny defects.
Findings
The effectiveness of the method is verified by public data sets NEU-CLS and DAGM 2007, and the steel ingot data set collected in the actual industrial field. The experimental results demonstrated that the proposed YOLOv4-Defect method can greatly improve the recognition efficiency and accuracy and reduce the size and computation consumption of the model.
Originality/value
This paper proposed an improved YOLOv4 named as YOLOv4-Defect for the detection of surface defect, which is conducive to application in various industrial scenarios with limited storage and computing resources, and meets the requirements of high real-time and precision.
中文翻译:
基于改进的YOLOv4的表面缺陷快速准确检测
目的
目前流行的基于卷积神经网络的图像处理技术具有计算量大、存储成本高、微小缺陷检测精度低的特点,这与工业应用所需的实时性和精度高、计算资源和存储受限. 因此,为了解决上述问题,提出了一种改进的 YOLOv4,称为 YOLOv4-Defect。
设计/方法/方法
一方面,本研究对YOLOv4的特征提取网络进行多维压缩处理,通过知识蒸馏来简化模型,提高模型的特征提取能力。另一方面,增加了一个具有更详细感受野的预测尺度来优化模型结构,可以提高对微小缺陷的检测性能。
发现
通过公开数据集NEU-CLS和DAGM 2007以及在实际工业现场采集的钢锭数据集验证了该方法的有效性。实验结果表明,所提出的YOLOv4-Defect方法可以大大提高识别效率和准确率,降低模型的大小和计算消耗。
原创性/价值
本文提出了一种改进的YOLOv4,命名为YOLOv4-Defect,用于表面缺陷的检测,有利于在存储和计算资源有限的各种工业场景中应用,满足高实时性和高精度的要求。