The Pandrol track fastener image is composed of two parts: track fastener clip sub-graph and track fastener bolt sub-graph. However, the detection of track fastener clip defect can be realized by track fastener image and track fastener image cannot effectively detect whether the bolt is loose. When the convolutional neural network is used to extract whole picture features and detect, many bolt features unrelated to the clips will be obtained, thereby resulting in a high false alarm rate. To solve these problems, a method based on local convolutional neural network to detect the Pandrol track fastener defects is proposed. First, the algorithm for automatic segmentation of track fastener pictures was used to divide the picture of the Pandrol track fastener into two sub-pictures, one sub-picture is the track fastener bolt and the other sub-picture is the track fastener clip. Second, convolutional neural network was used to detect the track fastener clip pictures. The influence of bolt features unrelated to clips on clips detection can be avoided through image segmentation for local feature extraction, thereby reducing the false alarm rate. Finally, the validity of the proposed method is verified using real Pandrol track fastener images.

Pandrol履带扣图像由两部分组成：履带扣夹子图和履带扣螺栓子图。然而，通过履带扣图像可以实现履带扣夹缺陷的检测，并且履带扣图像不能有效地检测螺栓是否松动。当使用卷积神经网络提取整个图像特征并进行检测时，将获得与剪辑无关的许多螺栓特征，从而导致较高的误报率。为了解决这些问题，提出了一种基于局部卷积神经网络的Pandrol履带扣件缺陷检测方法。首先，使用自动分割履带扣图片的算法将Pandrol履带扣的图片分为两个子图片，一个子图片是履带固定螺栓，另一个子图片是履带固定夹。其次，使用卷积神经网络检测履带扣件的图片。通过局部区域提取的图像分割，可以避免与片段无关的螺栓特征对片段检测的影响，从而降低了误报率。最后，使用真实的Pandrol履带紧固件图像验证了所提方法的有效性。