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The opening of high-speed railway brings a significant "time-space compression" effect, which has an important influence on the cooperative relationship and mode of tourism destinations along the route. Taking the Hefei-Fuzhou high-speed railway as an example, this paper studies cooperative network and mode of tourism destinations along the route by using the methods of social network, spatial analysis, descriptive statistics and software of Ucinet, GIS, SPSS, Excel and so on. Main conclusions are drawn as follows: (1) After the opening of the Hefei-Fuzhou high-speed railway, the tourists' cognition degree, the willingness to travel, the willingness to revisit the tourist destinations along the route have increased. The cooperative relation between different tourism destinations has been strengthened, and the cooperative network has formed a remarkable core- edge structure. The cooperative network between tourism destinations along the route is dense, and through the interconnection with other tourism destinations, a "group" of tourism destinations has formed along the route with stronger cooperation and greater influence. (2) The tourism flow between cities along the railway line is relatively large, with stable two- party relationship and tripartite relationship, forming three typical cooperation modes of "tourism core + tourism core", "tourism core + transportation core", "tourism core + transfer center". The three modes of cooperation present the characteristics of "strong alliance" and bring significant "Matthew effect", "filter effect" and "spillover effect". (3) The opening of high-speed railway provides an important opportunity for the establishment of cooperative relationship between tourism destinations along the route, collaborative development has become an inevitable trend in the future. In view of the current mode of cooperation, we should formulate various development strategies and establish a broader and deeper cooperative relationship between tourism destinations in order to realize the linkage development of tourist destinations. The results has certain enlightenment significance for enriching the research contents of China's high- speed railway tourism, revealing the cooperative network and typical model of tourist destinations along the high- speed railway, exploring the application of social network and spatial analysis methods in the research of cooperative relationship of tourism destinations, and formulating the regional tourism development strategy along the railway line. [ABSTRACT FROM AUTHOR]

The insulator is an important catenary component that maintains the insulation between the catenary and earth. Due to the long-term impact of railway vehicles and the environment, defects in the insulator are inevitable. Recently, automatic catenary inspection using computer vision and pattern recognition has been introduced to improve the safety of railway operation. However, achieving full automation of insulator defect detection is still very challenging due to the visual complexity of defects and the small number of defective insulators. To overcome these problems, this paper proposes a novel insulator surface defect detection system using a deep convolutional neural network (CNN). The proposed system consists of two stages. First, a Faster R-CNN network is adopted to localize the key catenary components, and the image areas that contain the insulators are obtained. Then, the classification score and anomaly score are determined from a deep multitask neural network that is composed of a deep material classifier and a deep denoising autoencoder. The defect state is determined by analyzing the classification score and anomaly score. Experiments of the catenary insulator defect detection along the Hefei–Fuzhou high-speed railway line indicate that the system can achieve high detection accuracy. [ABSTRACT FROM AUTHOR]