## 3 AYOLO Model (Attention YOLO)
### Information Fusion Module IFM  
The IFM plays a pivotal role in improving detection accuracy for small objects by bridging the semantic gap between low-level spatial details and high-level semantic features. Its dynamic fusion block ensures the efficient synthesis of multi-scale information, enabling precise detection, even in challenging scenarios. Moreover, by modulating channel numbers and incorporating the RepConv Block, IFM minimizes inference latency, making it highly suitable for resource-constrained environments, such as UAV-based real-time surveillance. Abstracting complex scale variations for improved object recognition: Because the scale of objects in camera-captured images varies with distance from the lens, object recognition systems must skillfully manage this complexity to maintain performance. The Information Fusion Module (IFM) within the AYOLO architecture is designed to overcome such scale-induced challenges, improving the model's ability to distinguish complex patterns between noise and semantic information disparities [63]. Overcoming the limitations of traditional feature fusion: Although low-level feature maps are adept at capturing location-specific details due to their smaller receptive fields, they are limited due to their sparse semantic content and high noise sensitivity. Conversely, high-level feature maps, although semantically richer, suffer from loss in fine-grained discrimination after extensive convolution operations. The traditional FPN heavily focuses on feature scale and ignores the need to distinguish between objects with varying levels of complexity in similar size ranges. AYOLO's interscalar fusion approach: AYOLO enables information to be transferred in a more detailed and efficient manner by filling the semantic gap between different layers. Figure 3: FAM architecture. IFM in AYOLO uses a dynamic fusion block that is effective in synthesizing high-level semantics with low-level spatial information, which is crucial for real-time object detection. Design innovations in the IFM--channel number variability: The IFM provides flexibility and control by modulating the number of channels for features at different scales; this minimizes model inference latency with negligible accuracy changes. RepConv Block integration: At the heart of the IFM is the RepConv Block, which assimilates feature maps from different layers and evolves them to produce outputs that are then forked for encoder processing [46]. Encoder processing and feature section: The encoder in the IFM processes and splits the combined feature map, allowing an iterative combination with different layer features. This splitting and subsequent recombination creates a rich, versatile feature output that intertwines with various layer features, creating a new output cascade for subsequent fusion. The IFM module architecture is illustrated in Figure 4.  
In addition to the improvements introduced by the IFM, we utilize a simplified bounding box regression loss to enhance localization precision during training. This loss function ensures that the model accurately aligns the predicted bounding boxes with the ground truth, as follows:  
\[L_{box}=\lambda_{coord}\sum_{i=1}^{S^{2}}\sum_{j=1}^{B}\ket{\epsilon_{ij}^{ obj}}\Big{[}(x_{i}-\hat{x}_{i})^{2}+(y_{i}-\hat{y}_{i})^{2}\Big{]}\]  
Here, \(L_{box}\) accounts for the discrepancies in localization while maintaining computational efficiency, aligning with the architectural principles of AYOLO. Impact of RepConv on Real-Time Processing  
RepConv plays a vital role in optimizing the Information Fusion Module (IFM) by enhancing feature synthesis across layers while maintaining real-time processing capabilities. During training, RepConv operates as a multi-branch structure, which is reparameterized into a single convolutional operation during inference. This reparameterization significantly reduces computational complexity, enabling AYOLO to achieve an inference speed of 239 FPS with minimal latency.