## 2 Related Work
### Small Object Detection  
Small target detection poses significant challenges and requires algorithms with powerful fine feature extraction capabilities [51]. Typically, the following two basic criteria are used to define small targets: absolute size and relative size. In terms of absolute size, targets with dimensions smaller than \(32\times 32\) pixels are classified as small. For relative size, subjects with an aspect ratio less than 0.1 times the original image size are considered small. Currently, small target detection algorithms fall into the following three main categories: those that use data augmentation, those that emphasize multi-scale learning, and those that leverage contextual information. Recent advancements demonstrate the effectiveness of integrating attention mechanisms into YOLO architectures, particularly for improving small object detection in agricultural environments. For instance, the study by [PERSON] et al., 2022 [52] introduces a novel attention-enhanced YOLO framework for detecting fruits in orchards. Their model leverages channel and spatial attention to refine feature extraction, significantly improving detection precision for small objects under challenging conditions such as occlusions and low resolution. Similarly, [PERSON] et al., 2024 [44] presents an attention-based YOLO model that integrates multi-scale attention modules to enhance the recognition of small objects in aerial imagery. These contributions align with the objectives of AYOLO by demonstrating the critical role of attention mechanisms in improving feature representation and localization accuracy. Data augmentation: Data augmentation strategies have been used to improve the representation of small targets within datasets and thus increase their importance in the network [53; 54]. This augmentation aims to strengthen the model's ability to effectively detect small targets. Multi-scale learning: Multi-scale learning techniques are used to improve network representation by combining shallow detail information with deep semantic information, thereby improving small target detection. However, it is important to note that multi-scale learning may increase model parameters and reduce inference speed. The Feature Pyramid Network (FPN) [24], introduced by [24] [PERSON] et al., is a well-established multi-scale learning network structure. In a FPN, the image undergoes bottom-up feature extraction followed by top-down feature fusion before being fed to the detection head for regression prediction. Small target detection was further improved by extending this approach with the Enhanced Feature Pyramid Network (EFPN) [55], which includes a feature texture migration module for ultra-high-resolution feature extraction. Use of contextual information: The integration of contextual information has been investigated as an effective strategy for small target detection. TPH-YOLOv5 [56] was introduced as a new approach that incorporates a transformer into the prediction header of YOLOv5 [56]. This integration enhances the predictive regression capabilities while using an attention mechanism to intensely focus on small targets. On a different note, it uses a query mechanism to speed up target detector inference. It leverages low-resolution features for preliminary localization predictions, which are then used to derive high-resolution features, contributing to increased accuracy in predictive regression. \"Attention-enhanced object detection for UAV-based small object recognition\" emphasized the importance of integrating attention mechanisms in improving object detection under challenging conditions such as low-resolution inputs and occlusions [44]. The article by [PERSON] et al. (2022) explores the integration of attention mechanisms into YOLO models to enhance detection accuracy for small objects in agricultural applications. Their method demonstrates how spatial and channel attention can significantly improve feature extraction in cluttered environments [32]. Similarly, [PERSON] et al.