In recent years, the object detection technology based on deep learning has made great breakthroughs, greatly improving the detection accuracy. However, most of the existing deep learning detection models are designed for multi-class object detection in natural scenes, which may lead to over-fitting when applied in structured specific railway scenes. Secondly, in order to meet the real-time detection requirements of high-speed comprehensive detection train with a speed of 350 km/h, the detection speed is put forward with extremely high requirements, and the existing deep learning model is difficult to meet the timeliness of high-speed detection. In this paper, we propose an optimized structured regions fully convolutional Networks (SR-FCN), which change the multiple small objects detection problem into single structured region location problem. The structured prior information of rail track is fused into the various processes of deep learning network including that sample construction, proposal region generation, network building and loss function constraint. By optimizing the regional proposal network as well as the anchor’s traversal number, the locating speed of the railway objects is greatly improved, and the locating error caused by local missing or background interference is avoided, which improves the robustness of detection. The experimental results show that the proposed SR-FCN network can not only achieve a high detection accuracy up to 99.99%, but also maintain a fast detection speed, which can meet the real-time detection at the high speed of 350 km/h.
The Integrated patrolling inspection train has been used worldwide for railway safety monitoring. The camera mounted under the train can capture the track image for abnormal fastener detection. For solving the high false positive alarm of rail fastener recognition arising from ballasts occlusion and non-uniform illumination, we proposed a fastener defect recognition method using deep learning model, and constructed four network structures based on AlexNet and ResNet to learn the fastener feature in complex background. The experimental results show that the RestNet18 network model with unfreezing convolutional layers not only performs well at the trained line, but also has good generalization at the new line, which is a more appropriate model for fastener recognition by comparison with the traditional handcraft feature and existing deep learning models.
The human vision system has abilities for feature detection, learning and selective attention with some properties of hierarchy and bidirectional connection in the form of neural population. In this paper, a multiscale Markov random field model in the wavelet domain is proposed by mimicking some image processing functions of vision system. For an input scene, our model provides its sparse representations using wavelet transforms and extracts its topological organization using MRF. In addition, the hierarchy property of vision system is simulated using a pyramid framework in our model. There are two information flows in our model, i.e., a bottom-up procedure to extract input features and a top-down procedure to provide feedback controls. The two procedures are controlled simply by two pyramidal parameters, and some Gestalt laws are also integrated implicitly. Equipped with such biological inspired properties, our model can be used to accomplish different image segmentation tasks, such as edge detection and region segmentation.
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