## 2 Related Work  
In object detection tasks, horizontal bounding boxes (HBBs) are typically used to detect targets and provide their categories.  
Many excellent HBB framework algorithms have been proposed in recent years, including the YOLO series [1], R-CNN series [9], RetinaNet [10], and FCOS [11].  
These methods have achieved good results in object detection tasks. Aerial image object detection faces many challenges such as arbitrary orientations, high target density, and a large resolution range.  
It is difficult for existing HBB algorithms to detect aerial image targets effectively; therefore, the aerial image object detection task converts the HBB into a rotatable bounding box (oriented bounding box; OBB) by adding a rotation dimension.  
Currently, oriented bounding box (OBB) methods are generally developed by improving upon HBB algorithms and can be categorized into two types: anchor-based [12] and anchor-free [13].  
Anchor-based methods typically require manual anchor settings, which not only introduce additional hyperparameters and model parameters but also directly impact model performance. In contrast, anchor-free methods eliminate the need for manual anchor settings, reducing reliance on prior information and offering greater adaptability.  
Neural Architecture Search (NAS) has been a hot topic in the field of deep learning in recent years for automatically designing efficient network structures; it is particularly suitable for scenarios that need to balance accuracy and computational complexity in aerial image target detection. The DAMO-YOLO [14] proposed by the Alibaba team uses MAE-NAS (Masked Autoencoder Neural Architecture Search) to optimize the detection backbone network while combining the extended neck structure and refined detection head design to further enhance the performance of real-time target detection. Additionally, [PERSON] et al. [15] proposed BuildingNAS. Different from previous NAS methods, they employed a hierarchical search space and proposed the Single-Path Sampling strategy to eliminate excessive GPU memory consumption in the searching process, achieving a great efficiency-accuracy trade-off. [PERSON] et al. [16] proposed a YOLO-NAS model that utilizes Neural Architecture Search (NAS) techniques. It addresses the limitations of previous YOLO models by introducing features such as quantization-friendly basic modules and sophisticated training schemes. This greatly improves performance, especially in environments with limited computational resources.  
Decoupling detection heads is another key research direction; this approach improves detection accuracy by separating classification and localization tasks. [PERSON] et al. [17] utilized a multi-scale task-adaptive decoupled head (MTAD) with varied receptive fields, enhancing detection accuracy by leveraging multi-scale features and adaptively generating relevant features for classification and detection. [PERSON] et al. [18] proposed a YOLO-SDH model, which utilizes a decoupled head that automatically adjusts the number of channels according to the model size, enhancing the network's detection effect by separating the classification and regression tasks. [PERSON] et al. [19] integrated a decoupled head into the YOLO head to untangle the classifiers and regressors, thereby accelerating network convergence and simultaneously improving network detection accuracy.  
From the above literature, it can be seen that architecture search can significantly reduce the computational complexity so that the aerial image target detection task can be more efficient, while the decoupled detection head not only accelerates the network convergence speed but also improves the detection accuracy. Based on the above, this paper synthesizes the anchorless decoupled detection head and Fast Architecture Search techniques to provide a new solution for the aerial image detection task.