SATII-DCGAN: Self-Attention Based Deep Convolutional Generative Adversarial Networks for Data Augmentation of Infrared Thermal Images  
[PERSON]  
1 College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213200, China; [EMAIL_ADDRESS] (Z.W. ); [EMAIL_ADDRESS] (Y.X) 1  
[PERSON]  
1 College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213200, China; [EMAIL_ADDRESS] (Z.W. ); [EMAIL_ADDRESS] (Y.X) 1  
[PERSON]  
1 College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213200, China; [EMAIL_ADDRESS] (Z.W. ); [EMAIL_ADDRESS] (Y.X) 1  
[PERSON]  
2 School of Engineering, Cardiff University, Cardiff CF24 3 AA, UK; [EMAIL_ADDRESS] 2  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/pdf/1009/109/](https://www.mdpi.com/journals/pdf/1009/109/)]([https://www.mdpi.com/journals/pdf/1009/109/](https://www.mdpi.com/journals/pdf/1009/109/))  
tests on aluminum foil packaging is of utmost importance. By implementing suitable testing techniques, such as infrared imaging technology, the sealing performance of aluminum foil can be accurately evaluated, thus ensuring product integrity and safety. (This not only protects consumers from potential health risks but also minimizes the economic losses associated with product recalls and contamination incidents.) Traditional methods for detecting the closure area of aluminum foil predominantly rely on manual inspections or single-point temperature measurements, as well as water detection methods, among others. Within the confines of a bottle, it is impossible to visually ascertain the presence of aluminum foil without unscrewing the cap. Nevertheless, this approach not only elevates production costs but also diminishes processing efficiency. On the other hand, the single-point temperature measurement method merely identifies the absence of aluminum foil and fails to assess the sealing performance of all contact surfaces, potentially resulting in missed detections. To effectively evaluate the sealing performance of the complete closed area, encompassing all contact surfaces of the aluminum foil sealing, an alternative solution utilizing infrared imaging technology can be employed. Infrared imaging enables comprehensive visualization of the shape and temperature distribution of the measured thermal images, addressing the shortcomings associated with the traditional methods of tightness detection in aluminum foil seals, including limited accuracy, high labor costs, and inherent defects. In recent years, deep-learning-based methods have proven to be effective for defect detection, due to their ease of use, cost-effectiveness, real-time capabilities, and accurate target detection [1; 2; 3]. However, similarly to other supervised deep learning approaches, object detection requires a significant amount of training data. To achieve high accuracy and generalization, an algorithm needs to be trained on diverse image datasets that contain various types of defects, allowing it to learn relevant features effectively. Unfortunately, in many industrial environments [4; 5], the majority of products meet production standards, and equipment typically remains in normal condition throughout its life-cycle. As a result, acquiring a substantial amount of labeled defect data becomes a challenging and time-consuming task, often accompanied by high costs or even feasibility issues. Effective neural network training for defect detection usually requires hundreds to thousands of images to achieve robust performance. However, production facilities for pesticides and pharmaceuticals are often located in remote areas, making it logistically challenging to collect and label large datasets. Moreover, the occurrence of defects is relatively rare, meaning that even when access to production sites is possible, the number of defect samples available may still be limited.