## 3 AYOLO Model (Attention YOLO)
### AYOLO Architecture  
The general view of the AYOLO architecture is shown in Figure 2 below. We are try to perform object detection and recognition by taking the YOLOv6 and DETR architectures as references. In YOLOv6, the FPN structure is divided into two branches to improve its ability to detect objects of different sizes. These branches successively produce large and small feature maps. Justification for Choosing YOLOv6  
The Semantic Aggregation, Enhancement, and Distribution Module (SAEDM) plays a critical role in enhancing feature representation by effectively combining semantic and spatial features across multiple scales. This mechanism minimizes information loss during layer transitions, a capability that is particularly important for detecting small objects, such as poppy capsules. By leveraging advanced techniques like dilated convolutions and pyramid architecture, SAEDM ensures robust multi-scale feature integration while maintaining minimal computational overhead. These improvements significantly enhance the model's precision in real-time applications. Although YOLOv11 represents the latest advancements in the YOLO series, AYOLO is deliberately based on YOLOv6 due to several key factors. YOLOv6 introduced notable innovations, such as the EfficientRep Backbone and Rep-PAN Neck, which have established a strong balance between speed and accuracy for real-time object detection. These features align closely with AYOLO's objectives of enabling UAV-based small object detection in resource-constrained environments. Furthermore, YOLOv6's modular architecture facilitates the seamless integration of advanced attention mechanisms, such as the SAEDM module, into AYOLO, without significantly increasing computational costs. Figure 2: General Architecture of the AYOLO Model. While YOLOv11 offers incremental improvements, including enhanced gradient path analyses and architectural refinements, these updates primarily address general-purpose object detection tasks. In contrast, AYOLO is specifically tailored for small-scale datasets and real-time applications, where the efficient foundation provided by YOLOv6 proves more suitable. A comparative analysis between YOLOv6, YOLOv11, and other state-of-the-art models, as shown in Table 1, highlights that AYOLO delivers competitive accuracy and inference speed for its targeted applications. In the table, upward arrows indicate that higher values positively impact performance, while downward arrows signify that lower values are more beneficial for performance. A residual network is often used as a backbone network to create feature maps with different resolutions. While low-resolution images contain a lot of semantic and channel information, high-resolution images contain a lot of spatial (location information) information. The FPN's top-down fusion strategy gradually mines the rich semantic information contained in high-level feature layers. The FPN's simple \(1\times 1\) convolution to reduce the size results in the loss of some channel information. This prevents the full use of channel information. The information fusion structure of the FPN essentially consists of multiple layers. In our model, we will provide information input through four stages for the first pyramid network and three stages for the second pyramid network. According to this information input layer, while information fusion is provided directly from adjacent layers, it is provided indirectly with information obtained from non-adjacent layers. Information overflow can cause significant information loss during calculation. In information interactions between layers, the information selected by the intermediate layers can be changed and some information can be discarded. In this case, we should not ignore the fact that information at a certain level can only help neighboring layers, while the information provided to other layers may decrease. As a result, the overall effectiveness of information fusion is limited. We update the traditional YOLOv6 architecture to overcome the above-mentioned issues.