School of Computer Science and Technology, North University of China, Taiyuan, China.
School of Information and Communication Engineering, North University of China, Taiyuan, China.
J Xray Sci Technol. 2022;30(4):709-724. doi: 10.3233/XST-211120.
The objective of this study is to apply an improved Faster-RCNN model in order to solve the problems of low detection accuracy and slow detection speed in spark plug defect detection. In detail, an attention module based symmetrical convolutional network (ASCN) is designed as the backbone to extract multi-scale features. Then, a multi-scale region generation network (MRPN), in which InceptionV2 is used to achieve sliding windows of different scales instead of a single sliding window, is proposed and tested. Additionally, a dataset of X-ray spark plug images is established, which contains 1,402 images. These images are divided into two subsets with a ratio of 4:1 for training and testing the improved Faster-RCNN model, respectively. The proposed model is transferred and learned on the pre-training model of MS COCO dataset. In the test experiments, the proposed method achieves an average accuracy of 89% and a recall of 97%. Compared with other Faster-RCNN models, YOLOv3, SSD and RetinaNet, our proposed new method improves the average accuracy by more than 6% and the recall by more than 2%. Furthermore, the new method can detect at 20fps when the input image size is 1024×1024×3 and can also be used for real-time automatic detection of spark plug defects.
本研究旨在应用改进的 Faster-RCNN 模型，解决火花塞缺陷检测中检测精度低和检测速度慢的问题。具体来说，设计了一个基于注意力模块的对称卷积网络（ASCN）作为骨干网络，以提取多尺度特征。然后，提出并测试了一种多尺度区域生成网络（MRPN），该网络使用 InceptionV2 实现了不同尺度的滑动窗口，而不是单个滑动窗口。此外，建立了一个包含 1402 张 X 射线火花塞图像的数据集。这些图像被分为两个子集，比例为 4:1，分别用于训练和测试改进的 Faster-RCNN 模型。所提出的模型在 MS COCO 数据集的预训练模型上进行了转移和学习。在测试实验中，所提出的方法的平均准确率达到 89%，召回率达到 97%。与其他 Faster-RCNN 模型，如 YOLOv3、SSD 和 RetinaNet 相比，我们提出的新方法的平均准确率提高了 6%以上，召回率提高了 2%以上。此外，该方法可以在输入图像大小为 1024×1024×3 时以 20fps 的速度进行检测，也可以用于火花塞缺陷的实时自动检测。
