## 1 Introduction  
Steel pipelines play an important role in gas and liquid transportation over long distances. However, due to the harsh environment of the construction locations, pipelines always suffer from structural damage problems caused by corrosion, cracks, etc. Pipeline corrosion has been defined for decades as a major source of pipeline deterioration in transmission lines [1]. In common engineering practices, corrosion crack identification highly relies on manual detection and subjective decision. In recent years, nondestructive evaluation (NDE) approaches have been proposed for corrosion crack identification in steel pipelines. NDE methods detect corrosion cracks of steel pipelines through different mediums, such as X-ray, gamma-ray radiography, ultrasonic, thermography, eddy current [2, 3], fiber optic distributed [4, 5] and electrical capacitance sensors [6, 7, 8, 9, 10, 11, 12]. However, due to the different needs for heat or wave sources and complicated data analyzers of different NDE methods, such applications can encounter various difficulties in terms of operational and monitoring requirements. Moreover, it may not be possible to achieve thorough monitoring of the wide fields [13]. Automatic detection technology of corrosion cracks has attracted increasing applications in actual projects. The early image processing algorithms based on binary image processing (BIP) technology are studied by pioneer researchers to detect the corrosion and cracks of steel pipelines. BIP extracts crack information by determining the optimal threshold, but due to the existence of noise information in steel pipeline images, BIP are still unable to accurately identify cracks [14]. Based on the strong edge features of cracks, some researchers have carried out research on crack identification algorithms based on edge features [15], studied image segmentation methods based on fractal features [16], and considered the comprehensive detection of the crack area and boundary information. However, for automatic identification of pipeline cracks, many algorithms have the problem of low accuracy and low speed. Real-time processing of pipeline images is difficult to be realized automatically. For mechanical and civil structures, real-time structural health monitoring is difficult when the types of structures are complicated or measured signals are corrupted due to environmental noise. Thus, model-based structural damage identification approaches are not effective. Deep learning can satisfactorily address this problem due to its superior adaptive learning of datasets [17; 18; 19; 20; 21]. A large and deep convolutional neural network (CNN) was employed to make a classification of 1.2 million high-resolution images by [PERSON] et al. [22]. Derivation and application of CNNs were presented by [PERSON] [23]. Real-time vibration-based damage detection and localization methods were proposed using CNNs [24; 25; 26; 27; 28; 29]. Wavelets were used to indicate damage occurrence of the structure under seismic load excitation by monitoring the mutations in the wavelet details of responses. [30; 31; 32]. A new signal-processing algorithm was formed by combining wavelets, neural networks and Hilbert transform, inspired by the deep learning model [33; 34; 35; 36; 37]. A Bayesian network was studied to evaluate the reliability of specific components according to their serviceability and inter-component correlations [38; 39; 40; 41; 42], and a hybrid response surface method was used [43]. Due to its flourishing development, deep neural networks have shown powerful capabilities to establish surrogate models with high accuracy and stability for crack detection. Taking the benefits of deep learning, an automatic crack identification method is proposed. The proposed method integrates the 3D-SM and convolutional deep neural network to extract the features of the 3D-SM images and identify the corrosion cracks.