# Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review
## 4 Deep Learning Defect Detection Methods for X-ray Images for Industrial Products
DR image features are then used to enhance the data, thereby increasing the diversity of the clarity and shape of defects. With depth separable convolution, models can be deployed on embedded devices with acceptable accuracy loss ranges. A method was presented in [131] that utilizes deep learning with X-ray images to detect defects in aluminum casting parts used in automobiles, with the goal of improving the accuracy of both the algorithm and data augmentation. The study found that using Feature Pyramid Networks (FPNs) resulted in a 40.9% increase in Mean of Average Precision (mAP) value, making it the most effective modification. Additionally, using RoAlign instead of RoI pooling in Faster R-CNN improved the accuracy of bounding box location. The study also proposed various data augmentation methods to compensate for the limited availability of X-ray image datasets for defect detection. The results showed that the mAP values for each data augmentation method reached an optimal value and did not continue to increase as the number of datasets increased. Overall, the proposed improvements to the Faster R-CNN algorithm resulted in better performance for X-ray image defect detection of automobile aluminum casting parts. Using the Faster R-CNN detection model with X-ray preprocessing was applied to the detection of tire defects in [132] to improve curve fitting performance. Faster R-CNN precision and recall of defects were improved by adjusting its feature extractor, proposal generator, and box classifier. According to literature [133], triplet deep neural networks can be used to detect weld defects. X-ray images are first preprocessed into relief images to make defects easier to identify. Following that, a deep network is constructed based on triplets, and a feature vector is obtained by mapping the triplets. The distance between similar defect feature vectors and the distance between different types of defect feature vectors must be closer. The SVM is also used for automatic detection and classification of weld defects. Based on the results of two experiments, the proposed method is capable of effectively detecting multiple defects. Tables 6 and 7 together provide a comprehensive overview of the current state of research and practices in the field of deep learning for defect detection in X-ray images. Table 6 summarizes recent research publications, and Table 7 compares the strengths and weaknesses of different techniques. This information can be valuable for anyone interested in the advancement of this field. \begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Method** & **Target** & **Performance** \\ \hline
[125] & Three-Stage Deep Learning & Engines & Accuracy rate: above 90\% \\
[126] & Convolutional Neural & & \\  & Network (CNN) & Welding & Recognition accuracy can be more than 90\% \\
[127] & Support Vector Machine & & \\  & (SVM) & Welding & Accuracy rate: 99.4\% \\
[128] & Support Vector Machine & & \\  & (SVM) & Welding & Rate of detection is approximately 99.1\% \\
[129] & Yolov5 & Insert Molding & Recognition accuracy: 93.6\% \\
[130] & Lightweight semantic & Tire & Achieved 97.1\% mIoU and 92.4\% L-mIoU for 512 \(\times\) 512 input images \\  & & Automobile & RoAlign showed a significant improvement in the accuracy of bounding box location compared to RoI pooling, resulting in an increase of 23.6\% accuracy under Faster R-CNN \\  & & & Compared with other methods, this method is capable of achieving a higher level of detection accuracy \\
[133] & Triplet Deep Neural Network & Welding & Can be more effective than traditional methods. \\  & & Aluminum & Can be effective in recognizing small and inconspicuous defects, with a 3.5\% improvement in mean Average Precision compared to RetinaNet \\  & & & \\  & & & \\  & & \\  & & & \\  & &