# Review on Computer Aided Weld Defect Detection from Radiography Images
## 6 Discussions
### Challenges  
Contrary to the development mentioned in the previous section, there are still challenges in the automation of weld defect inspection. The main contribution made in the last decade is the development of efficient and well-organized methods for feature extraction for constructing a robust classifier. The features providing enough discrimination should be selected. It is an important step in classifier design. Previous studies have mainly focused on the geometric and texture features while there are infinite unknown patterns and shapes for each type of weld defect.  
Although the deep neural networks introduced are dominant in learning the hierarchical feature from the weld images, the previous research show the drawbacks in developing a generalized defect inspection model based on these networks. There are mainly two aspects: the model training and the preparation of the datasets. Due to the huge model complexity behind deep learning methods, the training time of models is usually long which made it difficult to realize the real time defect detection. However, we believe that the implementation an on-line, real-time welding monitor system to prevent the possible defects from happening will be one of the key concerns in future. Moreover, the performance of deep models heavily depend on the scale and quality of datasets. For weld defect inspection, it is difficult to obtain a good dataset (a lots of weld images with defects being labeled by human). We believe that unsupervised representation learning provides good ideas for learning a layer of feature representations from unlabeled images. However, the literature involving the application of unsupervised representation learning in welding defect detection is still very few. Its effectiveness has yet to be supported and proven by more work.  
There is an intrinsic problem in the domain of weld inspection as in many other domains such as fraud detection, oil spill detection. It is just the class imbalance problem, namely the number of examples of one class is much higher than the others. The classifier tends to obtain high accuracy over the majority class, but poor accuracy over the minority class. Figure 5 presents that class imbalance leads to poorer performance when classifying minority class. The weld defect detection is just the problem of classification for minority class. [PERSON] carried out the identification of the defects successfully. However, they failed on the attempt of classifying the defects due to the unbalanced data generated by the few samples of each defect type [34]. [PERSON] studied the imbalanced data problem in the classification of different types of weld flaws. He used eight evaluation criteria to study the effectiveness of 22 data preprocessing methods for dealing with imbalanced data. The results indicated that some preprocessing methods do not improve any criterion, and they vary from one classifier to another [66]. These preprocessing methods solved the problem from only the perspective of data. The literature involving improving algorithms to deal with the imbalance learning for weld defect inspection is still few.