Aerial object detection continues to face significant challenges such as complex scene compositions, highly variable object sizes and densities, and diverse imaging perspectives. This study presents DroneDetect, a series of progressively enhanced models specifically designed to address these challenges in aerial detection tasks. To address the complexity of aerial scenes and enable effective semantic information extraction, we propose an efficient up-convolution block with a multi-branch auxiliary feature pyramid network that enhances the multiscale feature fusion capabilities. Building on this foundation, we address the critical need for precise spatial localization by introducing a Cross-Stage Receptive Field Attention module that integrates CSPNet with an improved receptive field attention convolution, enabling dynamic spatial attention mechanisms to capture fine-grained positional information. To ensure practical deployment efficiency while maintaining detection accuracy, we developed a lightweight shared detail-enhanced convolution detection head that optimizes parameter utilization and reduces the computational overhead. Extensive experiments on multiple aerial datasets demonstrate the effectiveness of the proposed approach. On VisDrone, DroneDetect-Enhanced achieved an AP of 24.05%, representing a significant improvement of 2.39% over the baseline YOLOv8s on the validation sets. The cross-validation results further validate the generalizability of our model, with performance gains of 3.9% on UAVDT, 3.0% on CARPK, and 1.5% on DIOR. Notably, DroneDetect-Enhanced maintains comparable or reduced computational complexity while using fewer parameters than the baseline. Comprehensive ablation studies and comparative analyses with state-of-the-art methods confirm that our approach effectively balances the accuracy and efficiency of real-world aerial object detection applications. This code is available at https://github.com/aerialCV/DroneDetect.

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