# Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review
## 6 Discussion
* Multi-modal sensor integration:  
Defect detection in industrial products often relies on visual inspections using cameras or other imaging devices. However, incorporating other types of sensors, such as thermal, acoustic, or vibration sensors, can provide additional information that can aid in the detection of defects. Research in this area could focus on developing methods for integrating data from multiple sensors and using it to improve the accuracy of defect detection. This could include techniques such as sensor fusion, where data from multiple sensors is combined to provide a more comprehensive view of the product, or methods for combining deep learning with other types of sensor data, such as sensor data from IoT devices. * Continuous learning:  
In industrial environments, the product line is frequently updated, and new defect types are introduced. Research in this area could focus on developing methods for continuous online learning, which can be used to adapt the defect detection system as new data is acquired and new defects are introduced. This could include online learning techniques, where the system can continuously update its knowledge as new data is acquired, or active learning methods, where the system can actively select the most informative images for annotation. This would allow the system to adapt to changes in the product line and improve its performance over time. * Real-time detection:  
There are only a few existing defect detection methods that are implemented in real time. In order to apply these methods to real-time inspection scenarios in the future, computationally efficient methods must be developed among these methods in order to achieve detection success rates in real time. * Reducing the complexity:  
Users of defect detection methods are interested in understanding why a defect has been missed or incorrectly identified in an acceptable part when such a method fails to find the defect. The majority of deep learning methods follow a complex architecture, so humans have difficulty understanding the decision-making process and providing a rationale for failure. When it comes to deploying and improving the performance of a system, this can be a challenge. Moreover, in industrial applications, lightweight deep learning networks will be easier to deploy. Often, the processing resources used to support artificial intelligence computations are valuable in quality inspections on production lines and industrial maintenance monitoring. By using lightweight networks, the prediction system's workload can be effectively reduced, which is extremely beneficial for simple terminal deployments and can also reduce costs and performance. * A common reference database: Testing can be conducted on different databases, though several studies have failed to provide satisfactory results due to inconsistency in such databases and a lack of testing samples. Additionally, most of the studies presented in this review have their own databases with varying sizes and quality. To evaluate and compare performance in the future, a common reference database would be helpful.