How Much Data Is Required for a Transformer-Based Infrared Small Target Detection?

In most state-of-the-art (SoTA) infrared small target detection algorithms, image regions are processed locally. More recently, some transformer-based algorithms have been proposed that account for separate image regions to detect small objects. Besides their success, transformer-based algorithms require more data when compared to classical methods. In these algorithms, massive datasets are used to achieve comparable performance with the SoTA methods for the RGB domain. There is no solid work in the literature about how much data is required to develop a transformer-based small target detection algorithm. By its nature, a small target does not contain discriminative contextual information. Thus, its blob-like shape and the contrast difference between the target and background are the main features exploited by the literature. Analyzing the required amount of data to obtain acceptable accuracy for infrared small target detection is the main motivation of this study.