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.
There are three main problems in track fastener defect detection based on image: (1) The number of abnormal fastener pictures is scarce, and supervised learning detection model is difficult to establish. (2) The potential data features obtained by different feature extraction methods are different. Some methods focus on edge features, and some methods focus on texture features. Different features have different detection capabilities, and these features are not effectively fused and utilized. (3) The detection of the track fastener clip will be interfered by the track fastener bolt subimage. Aiming at the above three problems, a method for track fastener defects detection based on Local Deep Feature Fusion Network (LDFFN) is proposed. Firstly, the track fastener image segmentation method is used to obtain the track fastener clip subimage, which can effectively reduce the interference of bolt subimage features on the track fastener clip detection. Secondly, the edge features and texture features of track fastener clip subimages are extracted by Autoencoder (AE) and Restricted Boltzmann Machine (RBM), and the features are fused. Finally, the similarity measurement method Mahalanobis Distance (MD) is used to detect defects in track fasteners. The effectiveness of the proposed method is verified by real Pandrol track fastener images.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.