# Review on Computer Aided Weld Defect Detection from Radiography Images
###### Abstract  
The weld defects inspection from radiography films is critical for assuring the serviceability and safety of weld joints. The various limitations of human interpretation made the development of innovative computer-aided techniques for automatic detection from radiography images an interest point of recent studies. The studies of automatic defect inspection are synthetically concluded from three aspects: pre-processing, defect segmentation and defect classification. The achievement and limitations of traditional defect classification method based on the feature extraction, selection and classifier are summarized. Then the applications of novel models based on learning(especially deep learning) were introduced. Finally, the achievement of automation methods were discussed and the challenges of current technology are presented for future research for both weld quality management and computer science researchers. radiographic image; image processing; feature extraction; classifier; deep learning; defect detection +
Footnote †: journal: Medical  
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; 4; 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.