Deep learning-based methods for detecting targets in images have advanced significantly in recent years and shown to have tremendous application potential across various domains. But in real-world applications, these methods are frequently constrained by the lack of labelled samples, particularly in situations with small sample sizes. As a result, it has become a crucial area of research to determine how to carry out target detection efficiently. This paper conducts a thorough analysis of image target detection technology under small sample conditions. It does this by methodically elaborating on the definition of the problem of small-sample image target detection and by thoroughly discussing a number of small-sample learning approaches, the most prominent of which are data enhancement-based, migration learning-based, and meta-learning-based. By discussing various algorithms and the fundamental idea behind the process, the benefits and drawbacks of the current approaches to small-sample detection are made evident. Sample identification issues. Lastly, the technological challenges of the current small sample target identification and the direction of future improvement are explored.