## 1 Introduction  
The detection of small targets using infrared technology is vital in multiple fields, such as terrestrial surveillance [1], alert systems [2], and accurate direction [3], among others. Compared to traditional object detection tasks, small object detection has distinct characteristics. Small object detection stands out from conventional object detection tasks due to its unique features. Initially, owing to the diminutive dimensions of the objects or extended distances in imaging, the percentage of pixels taken up by the object in the image is minimal, and, in severe instances, it might consist of merely a handful of pixels [4, 5]. Additionally, due to the considerable disparity in the pixel space between the target and background areas, smaller infrared targets show reduced signal-to-noise ratios and signal-to-clutter ratios amidst a densely cluttered backdrop [6]. Additionally, when local aliasing occurs between the target and background, local feature loss can occur [7]. These characteristics make small object detection particularly challenging. Notable limitations persist in identifying faint and diminutive infrared targets against intricate backdrops, which can be encapsulated as follows: Initially, the uneven spread of cluttered backdrops and smaller targets leads to the easy loss of data from smaller targets in downsampling, and shallow elements with more target details are frequently missed. Furthermore, partial concealment of targets leads to complications like the loss of local data and changes in shape, complicating the identification, recognition, and pinpointing of these targets. Thirdly, as the imaging distance changes, the pixel range of the target can vary from just a few pixels to thousands. For small targets, localization loss functions based on the IoU and its various variants, such as the GIoU [8], DIoU [9], and CIoU [10], are highly sensitive to prediction deviations, leading to difficulties in boundary box regression convergence. In response to the previously mentioned challenges, this document introduces a sturdy network for feature improvement designed to identify faint and diminutive targets amidst intricate backdrops. In particular, the detection network's neck introduces a module called Iterative Attentional Feature Fusion (iAFF), employing a multi-scale channel attention mechanism (MS-CAM) to address the challenge of overpowering small target features in the process of cross-scale attention feature fusion. Furthermore, we suggest integrating the Occlusion-Aware Attention Module (OAAM) with the Dynamic Head's scale and spatial focus, which dynamically discerns the significance of each semantic layer, tackling issues like local aliasing and loss of target features. Addressing the issue of convergence challenges in localization loss for minor targets in standard detection algorithms, we utilize a two-dimensional Gaussian distribution for reconstructing bounding boxes and apply Normalized Wasserstein-Gaussian Distance (NWD) to lessen the impact of the IoU on small target localization loss sensitivity. In the end, comprehensive ablation experiments are conducted with the recommended modules on RetinaNet, YOLOv8m, and YOLOv11m, and the results are compared with other SOTA algorithms to confirm the effectiveness of the proposed technique. A brief summary of the paper's contributions follows:  
1. Comparative research is conducted to evaluate the amalgamation of multi-scale feature maps in the FPN and PAN against the newly introduced iAFF module for feature merging. Findings confirm that our suggested network, through the consolidation of diverse contextual data across channel dimensions, is capable of highlighting both extensive targets with worldwide distribution and minor targets with localized distribution. This method enhances the network's capacity to identify and sense objects amidst significant scale fluctuations. 2. After amalgamating features from various scales, it is proposed that the OAAM enhances the interconnectivity among feature channels dynamically.