## 2 Related Work  
Various techniques for identifying tiny infrared objects based on mathematical models have been proposed, as shown in Table 1, including filter-focused strategies [11, 12], localized contrast methods [13, 14], and low-rank and low-rank decomposition techniques [15, 16]. Methods based on filters divide targets through background estimation and target enhancement. Nonetheless, their use is confined to consistent backdrops and is not robust against intricate backdrops. Targets are pinpointed through local contrast-based techniques that assess the intensity variance between the target and its adjacent area. Nonetheless, they face challenges in accurately identifying dim targets. Low-rank decomposition techniques employ the sparsity of the target and the low-rank characteristics of the background to distinguish the structural elements of the target from the background. However, in scenarios involving images with intricate backdrops and diverse target forms, these techniques show a significant frequency of incorrect alerts and overlooked detections. Under real-world conditions, infrared imagery frequently presents intricate backdrops, faint targets, and considerable scale discrepancies, complicating the effectiveness of the aforementioned techniques in identifying faint and minor infrared targets. Lately, the field of deep learning has seen remarkable advancements and major breakthroughs across various disciplines. Differing from traditional techniques for identifying tiny infrared objects, deep learning utilizes an all-encompassing, data-focused learning strategy to dynamically acquire characteristics for these targets, thus obviating the necessity for manually generated features. [PERSON] and others endeavored to balance the metrics for detecting missed and false alarms, and proposed the development of a conditional generative adversarial network (GAN) comprising two generators and one discriminator [17]. Utilizing a context aggregation network, the pair of gener \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Mathematical Models Based** & **Deep Learning Based** \\ \hline \multirow{5}{*}{**Box regression**} & MMF [11] & MDvsFA [17] \\  & MIP [12] & ACM [18] \\  & IRHVS [13] & ISTDNet [19] \\  & RLCM [14] & IRSTFormer [20] \\  & NRAM [15] & OCRNet [21] \\  & IPM [16] & FCOS [22] \\ \cline{3-4}  & &
\begin{tabular}{c} IFEA [23] \\ DTUNet [24] \\ MDLE [25] \\ RDANet [26] \\ FSENet [27] \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related work list. fields varying in size to achieve both local and global segmentation outcomes for the target. The discriminator differentiates between the three segmentation results obtained from the pair of generators and the genuine annotations of the image. The technique presents an innovative deep learning structure aimed at lowering the rate of both missed and false alarms in detection algorithms, yet it is unsuitable for situations characterized by intensely cluttered backgrounds and localized aliasing between targets and backgrounds. [PERSON] and others proposed a unique module for asymmetric context modulation to identify tiny infrared objects, enhancing the interaction between overarching semantics and minute details via a point-by-point channel attention approach [18]. [PERSON] and others developed a framework for extracting features that merges artisanal feature techniques with convolutional neural networks [19]. [PERSON] and others developed a layered overlapping compact patch transformer (HOSPT) as an alternative to the CNN for encoding features of multiple scales from a single-frame image [20]. [PERSON] and others launched a single-phase cascade refinement network (OSCAR) to address the issue of incorrectly identifying minor targets as background [21]. [PERSON] and others suggested a proficient single-phase infrared method for detecting small targets aimed at discerning spatio-temporal correlations in image sequences utilizing enhanced FCOS (Fully Convolutional One-Stage Object Detection) [22].