## 1 Introduction  
In recent years, there has been significant advancement in convolutional neural network (CNN)-based object detection [1; 2; 3; 4; 5], driven by the continuous development in deep learning [6; 7]. Object detection plays a crucial role in computer vision tasks, as it combines classification and localization, laying the foundation for various challenges such as semantic segmentation, image understanding, and object tracking. Furthermore, object detection is extensively employed in various applications such as autonomous driving, facial recognition, UAV navigation, intelligent transportation systems, and remote sensing image analysis. In contrast to general object detection methods, our research concentrates on drone aerial object detection, an inherently more challenging task due to the presence of numerous small and multi-scale objects. Consequently, enhancing the ability of the backbone network to extract features of small objects and reinforcing the representation of small object features are the primary challenges in object detection under UAV scenes. Current state-of-the-art (SOTA) detection networks can be classified into one-stage and two-stage models [8; 9; 10]. The two-stage detector initially generates region proposalsfor unknown categories and then conducts classification and regression on these candidate regions to achieve object classification and localization, exemplified by Faster R-CNN [11], Cascade R-CNN [12], and so on. To achieve highly efficient real-time object detection, the one-stage detector performs both regression and classification tasks simultaneously. This approach simplifies the model's complexity, leading to improved inference speed, such as in the YOLO series [13; 14; 15; 16; 17] and RetinaNet [18]. While these detection models have demonstrated remarkable performances on natural image datasets, their application to UAV aerial images often results in significant degradation in the detection performance, primarily due to insufficient feature detail expression. [PERSON]. [PERSON] et al. proposed the Feature Pyramid Network (FPN) [19] to create a multi-layer feature map by incorporating deep and shallow features, which can facilitate subsequent object detection tasks. However, in scenarios where large objects coexist in close proximity to small objects, the large objects' saliency tends to overshadow the features associated with the smaller objects. This results in a weakened representation of the small objects' features, leading to a less effective detection of these objects. To address the challenges mentioned above, this paper proposes a novel anchor-free design, the AFE-YOLOv8 model, for object detection from images captured in UAV scenes. The model introduces three innovative structures: the small-object-aware module called AFEM, salient feature enhancement module named MNFM, and the multi-scale object-matching module called RFEM as shown in Figure 1. Our AFEM draws inspiration from the detection networks of QueryDet [20] and TSODE [21], which are well established to encompass more high-level semantic information pertaining to small objects compared to shallow layers. The AFEM aims to tackle the issue of inadequate feature expression in the small-object pipeline by enhancing the feature representation of the small-object pipeline through utilizing high-level semantic information contained in the medium- and large-object pipelines' features. Furthermore, we introduce the Multi-scale Nonlinear Fusion Module (MNFM) to enhance the detection performance of small targets by capturing salient feature representations through nonlinear mapping and multi-scale fusion operations. The nonlinear mapping unit of the Nonlinear Fusion Module (NFM) is constructed using stacked deformable convolutions [22; 23], which can adaptively adjust the shape of the convolution kernel, making it highly suitable as a fundamental unit for nonlinear mapping in order to extract salient features of the small target.