**1. Introduction**

Welded structures have been widely used in many areas, such as construction, vehicle, aerospace, railway, petrochemical and machinery electrical. The weld defects are inevitable due to the different environmental conditions and welding technology in the welding process. It is critical to check the quality of welded joints to assure the reliability and safety of the structure, especially for those critical applications where weld failure can be catastrophic. As the most commonly used methods to detect the quality of welding, nondestructive testing techniques (NDT) include radiographic, ultrasonic, magnetic particle and liquid penetrant testing methods. In this paper, we mainly pay attention to radiography testing technology commonly used to inspect the inner defects of welds. The X-ray and Gamma-ray sources are usually used to produce the radiographic weld images by penetrating the weld structure and exposing photographic films.

Weld flaws are described by the variation of intensity in the radiographic films. These films should be checked by certified inspectors to evaluate and interpret the quality of welds called human interpretation. However, the radiogram quality, the welding over-thickness, the bad contrast, the noise and the weak sizes of defects make difficult the task [1]. There are some drawbacks for human interpretation. Firstly, the inspectors are generally trained and have relevant expertise and experience. However, it is still difficult for the skilled inspector to recognize the small flaws within a short time. Secondly, the human interpretation is usually short of objectivity, consistency and intelligence. Finally, The labor intensity of human interpretation is large because lots of films are produced each day due to the improvement of production efficiency in modern industry. In addition, human visual inspection is,

at best, 80% effective, and this effectiveness can only be achieved if a rigidly structured set of inspection checks is implemented [2]. Thus many researchers began to build the intelligent systems based on computer which help human on evaluating the quality of welds before the 1990s. Such computer-aided systems typically take the digital images as the object to extract the welds and detect the flaws in the images by various algorithms. Thus, for the conventional films, the digitization should be necessary. Fortunately, digital radiography systems (digitizers) are currently available for digitizing radiographic films without losing the useful information of the original radiograph. Unlike the conventional films which can only be evaluated manually, the digitized radiographic images not only enable the storage, management and analysis of radiographic inspection data easier, but also make the more intelligent inspection of welds possible.

The Advanced Quality Technology Group of Lockheed Martin Manned Space Systems had been supporting three projects which contribute to the building of computer-assisted X-ray film interpretation system, development of weld flaw detector based on image processing and using of Geometric Arithmetic Parallel Processor (GAPP) chips [3–5]. Automatic detection method for weld flaws have rapidly advanced in recent decades. This benefits from the development of technologies such as image processing, computer vision, pattern recognition and deep learning which improved the analysis capability for images.

In the initial study, many researchers took the intensity plot of the line image as the object, and processed the 2D image line-by-line. These methods are based on observation that the weld defect would destroy the bell shape possessed by the line image of good weld. Thus the detection is to find the abnormity of intensity plot. The features used for detection and classification are often defined in the intensity plot. The defects can be discriminated accurately. However, these methods are often time-consuming due to their processing style for images. In addition, it is difficult to recognize diverse types of weld defects. Then most detection systems based on 2D images relied on the image processing, feature extraction and classification. Various image processing technologies were successfully applied to improve the quality of images and remove background to highlight defect region. The geometric features, texture features or combination of both features were applied to characterize the shape, size and texture of defects for further classification. The MFCCs together with polynomial features were also used for defect identification due to their robust to noise and time shifts in signals. The feature selection is usually used between feature extraction and classifier for reducing the number of features to save the computational costs. Furthermore, developments in computer hardware technology and representation learning has provided the perfect conditions for automation of weld defects inspection. Especially, with recent advances of deep learning theory, in optical image recognition domain, considerable effort has been made to design multistage architectures which learn the hierarchical features from images automatically.

This paper aims to review the common practices for weld defects detection and classification based on the digitized radiographic images. The radiation involved in these studies is X-ray (or sometimes Gamma-ray). The two radiographic sources are used in different occasions which are not distinguished in this paper. The paper focuses on the summary of analysis methods for digitized radiographic images. It gives a detailed and comprehensive summary of literatures from image pre-processing, defect segmentation and defect classification. It elaborates from four aspects: (1) the quality improvement of weld images; (2) traditional techniques for defect detection and classification; (3) the application of novel models based on learning; and (4) the achievements and challenges of current methods.
