Accurate fastener positioning and state detection form the prerequisite for ensuring the safe operation of rail track. The demands for intelligent, fast and accurate detection cannot be satisfied by traditional methods using image processing and fastener classification. In view of this, a two-stage classification model based on the modified Faster Region-based Convolution Neural Network (Faster R-CNN) and the Support Vector Data Description (SVDD) algorithms is proposed in the paper for fastener detection. Firstly, the data set of detection images is built with the images being labeled, and the classification and detection model based on Faster R-CNN is constructed according to the characteristics of practical fastener images. The anchor box optimization function is established by labeled data set to optimize the box of region proposal network in the model, to enhance the detection rate and accuracy of detection. Then, according to the detection result by Faster R-CNN, the SVDD algorithm is applied for the second stage classification of deviated fasteners, which avoids inaccurate classification caused by different deviated angles of fasteners. Through the verification and analysis of practical detection case, it is verified that the proposed method can improve the efficiency and precision of fastener detection with higher detection rates and accuracy in comparison with other baseline detection methods, making it suitable for fast and accurate detection of fastener states.

准确的扣件定位和状态检测是保证轨道安全运行的前提。传统的图像处理和紧固件分类方法无法满足智能、快速、准确检测的需求。鉴于此，本文提出了一种基于改进的基于快速区域的卷积神经网络（Faster R-CNN）和支持向量数据描述（SVDD）算法的两阶段分类模型用于紧固件检测。首先，利用被标记的图像构建检测图像数据集，并根据实际紧固件图像的特点构建基于Faster R-CNN的分类检测模型。锚框优化函数是通过标记数据集建立对模型中区域提议网络的框进行优化，以提高检测率和检测准确率。然后，根据Faster R-CNN的检测结果，将SVDD算法应用于紧固件的第二阶段分类，避免了紧固件偏离角度不同造成的分类不准确。通过实际检测案例的验证和分析，验证了该方法与其他基线检测方法相比，能够提高紧固件检测的效率和精度，检测率和准确度更高，适用于紧固件的快速准确检测。状态。根据Faster R-CNN的检测结果，对偏斜紧固件的第二阶段分类采用SVDD算法，避免了紧固件偏斜角度不同造成的分类不准确。通过实际检测案例的验证和分析，验证了该方法与其他基线检测方法相比，能够提高紧固件检测的效率和精度，检测率和准确度更高，适用于紧固件的快速准确检测。状态。根据Faster R-CNN的检测结果，对偏斜紧固件的第二阶段分类采用SVDD算法，避免了紧固件偏斜角度不同造成的分类不准确。通过实际检测案例的验证和分析，验证了该方法与其他基线检测方法相比，能够提高紧固件检测的效率和精度，检测率和准确度更高，适用于紧固件的快速准确检测。状态。