## 5 Discussion and Conclusions  
AYOLO architecture; It marks a significant advance in object detection technology. Real-time deep learning models were analyzed and integrated, and by combining them with the attention mechanism, a versatile and powerful tool was created. Its improved sensitivity, latency, and accuracy rates demonstrate its effectiveness in accurately identifying objects, while its speed ensures applicability in real-time scenarios. A model with two pyramid structures is proposed for real-time object detection. Various modules such as FAM, IFM, and ITEM have been integrated to increase the feature extraction ability for semantic segmentation. It extracts multi-scale global semantic information through a multi-scale image transformer. The fusion module efficiently integrates multi-scale local semantics and multi-scale global semantics. The architecture's robustness across a variety of conditions addresses one of the biggest challenges in object detection. This makes AYOLO suitable for a variety of applications, from autonomous vehicles to surveillance systems. Moreover, its scalability and adaptability to specific requirements highlight its potential for a wide range of industrial and research applications. However, there are limitations to consider. Increasing the complexity of the model may require more computational resources, which can be a barrier to deployment in resource-constrained environments. Future work will focus on optimizing the architecture to provide lower resource consumption without compromising its performance. In order to solve the problem of insufficient feature extraction ability for semantic segmentation, a binary network is proposed. Multi-scale global semantic information is extracted through a multi-scale image transformer. This effectively expands the effective receptive field of the network. In this way, the network is guaranteed to have rich semantic information. We enabled the detailing of small objects by using relative positional coding instead of one-dimensional absolute positional coding. Thus, we struck a balance between accuracy and inference speed. Due to the attention mechanism, we increased accuracy by combining global information and semantic information. In our future works, we will continue to investigate how rich semantic information can be extracted and the balance between detail and semantic information. Real-time object detection, especially for small plants, has been studied in detail due to the problem of insufficient feature extraction ability. A dual network structure with two pyramid structures has been proposed for real-time plant recognition. Multi-scale global semantic information was extracted, and a multi-scale image transformer was used for inference. A hybrid structure is proposed by efficiently injecting multi-scale local semantic information and multi-scale global semantic information. Thus, the network has semantic information. By proposing a new design, that is, by using the relevant feature of global and local information, the feature representation ability of semantic information is increased. The experimental results have shown that the proposed network provides a balance between accuracy and inference speed. Our work on semantic information extraction and detailed feature detection continues. The integration of attention mechanisms, as demonstrated in recent studies [44; 52], highlights the transformative potential of this approach for small object detection. Our AYOLO model builds upon these advancements by adopting a hybrid architecture that incorporates attention mechanisms to optimize feature extraction and fusion, particularly for UAV-based detection tasks. Future work will focus on refining the AYOLO architecture by enhancing feature extraction with multi-scale attention modules and exploring transformer-based enhancements. These improvements aim to capture fine-grained object details and boost detection accuracy.