## 4 Experiments
### Technology Comparison  
To evaluate the effectiveness of the AYOLO model trained with data augmentation, we compared its performance with the latest object detection algorithms, as summarized in Table 1. The evaluation focuses on key metrics such as Average Precision (mAP) at different IoU thresholds (AP0 and AP95) and performance on object sizes (APs, APm, and API). Even when compared to the latest YOLO version, YOLOv11, AYOLO with data augmentation demonstrates superior performance across all key metrics. While YOLOv11 achieves an AP0 of 82.5% and APs of 20.8%, AYOLO improves these to 85.1% and 23.4%, respectively, showcasing its enhanced precision for small object detection. Furthermore, AYOLO's AP95 of 50.8% exceeds YOLOv11's 48.9%, reflecting its improved localization under stricter IoU thresholds. Notably, these improvements are achieved without additional computational requirements, as AYOLO maintains similar parameter and FLOPS efficiency. These results confirm the improved effectiveness of AYOLO in real-time applications, particularly in scenarios requiring the precise detection of small objects, such as UAV-based surveillance. Future work could focus on optimizing AYOLO for larger datasets to further enhance its scalability and applicability. To comprehensively validate the overall detection ability of the model presented in this article, comparison data with various state-of-the-art target detectors are included in Table 1 below. The table shows performance metrics for various architectures, including input size, frames per second (FPS), and latency. Input size: The size of input images used to test architectures. FPS (Frames Per Second): The number of frames per second that each architecture processes. Higher FPS values indicate faster rendering. Latency: The time each architecture takes to process a single frame, measured in milliseconds (ms). Lower latency values indicate faster processing. When we compare the architectures, we see the following:  
The comparison shows that AYOLO-M achieves a slightly higher FPS rate with a small increase compared to other architectures (see Table 2). This improvement shows that AYOLO-M may offer slightly better real-time processing capabilities than the other models listed. However, it is important to consider other factors, such as model complexity and accuracy, when determining the most appropriate architecture for specific applications. The table shows AYOLO architecture's performance metrics strike a balance between speed and accuracy. In Table 3, we see that the smallest variant, AYOLO-S, reaches the highest FPS of 300 while maintaining a relatively low latency of 4.9 ms. However, its AP is slightly lower compared to larger variants. The largest variant, AYOLO-XL, reaches the highest AP at 0.92 but has the lowest FPS at 118 and the highest latency at 9.6 ms. AYOLO-M and AYOLO-L strike a balance between speed and accuracy, while AYOLO-M offers a good compromise between FPS, latency, and AP. We compared different normalization techniques on the architecture. We used the parameters accuracy, mean Average Precision (mAP), inference time, and model complexity (FLOP) to compare their impact on the AYOLO architecture. As shown in Table 4, BatchNorm outperforms both LayerNorm and RMSNorm in terms of accuracy and mAP, while also having the fastest inference time. LayerNorm has the lowest accuracy and mAP among the three but is close in performance, with a slightly higher inference time. RMSNorm provides a balance between BatchNorm and LayerNorm, offering moderate accuracy and mAP, with a slightly higher inference time than BatchNorm but lower than LayerNorm. These results highlight the efficiency of BatchNorm in the AYOLO architecture, making it a preferred choice for real-time object detection tasks. However, depending on the specific application requirements, RMSNorm and LayerNorm may still be viable alternatives.