# Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review
## 6 Discussion  
Deep learning technology has revolutionized the field of defect detection in industrial products. However, finding a suitable deep learning model for solving the defect detection problem is very difficult due to the particularities of industrial scenarios. In the coming years, deep learning will encounter challenges and trends as it becomes more widely used in industrial fields. A brief description of recent trends and future research directions is provided in this section. * Integrating deep learning with other methods: By incorporating other techniques such as traditional image processing, the robustness and performance of the defect detection system in challenging conditions can be enhanced. For instance, using traditional image processing techniques to preprocess the images before inputting them into a deep learning model can improve the quality of the data and make it easier for the model to effectively detect defects. Additionally, integrating deep learning with other techniques, such as physics-based simulations, can provide better understanding of the underlying physical causes of defects and lead to the development of more efficient and effective defect detection methods. * Adjustment to various lighting scenarios: Examining industrial products frequently occurs under diverse lighting conditions, which can make it hard to identify defects. Research in this field could concentrate on developing techniques for adapting to various lighting conditions and using them to enhance the precision of defect detection. This could include methods such as image enhancement techniques, color constancy techniques, and multiple exposure fusion techniques, to improve the visibility of defects in different lighting conditions. Additionally, research could also focus on developing deep learning models that are robust to changes in lighting conditions, such as using adversarial training methodologies, to improve the robustness of the model. This may lead to a more accurate and reliable defect detection system that can function in a wide range of lighting scenarios. * Transparent AI: To be implemented in industrial environments, defect detection systems need to be transparent and explainable. Research in this field could focus on developing techniques to make deep-learning-based defect detection systems more understandable, so that users can comprehend why a defect was missed or incorrectly identified. * All aspects need to be taken into account: In order for a defect detection system to perform well, it must take into account various factors. There are many factors that can influence the accurate detection of defects, such as defect size, shape, the technique for image acquisition, alignment and distortion of images,resolution of images, and algorithmic speed, among others. It is important to consider all of these factors when creating a mature and successful method. * Limited number of defect samples:  
In many industrial applications, deep learning methods require a large training dataset and have high computational costs, and the number of defect samples is often insufficient to detect defects. Additionally, as the product line is frequently updated, new defect types are introduced and the detection process becomes more challenging. When training on normal samples, a simple defect detection method does not have any issues dealing with a small defect dataset, but, when it comes to defect localization and classification, the size of the dataset containing defects can be a challenge. * Utilizing transfer learning:  
Defect patterns may be shared between two different application domains. There may be similarities in the morphology of cracks in two different materials, but they may be different in their sizes and colors. It is currently necessary to train two different networks in order to use current approaches. A well-trained, tested network can transfer its knowledge to a new network to speed up the training process. Currently, transfer learning is not effectively utilized in most approaches.