Axis-Squeeze and Multirouting Scale-Adaptive Fusion Network for Remote Sensing Images Object Detection

Complicated background and small object issues are the primary challenges currently confronting remote sensing object detection (RSOD). To tackle the aforementioned issues in RSOD, we propose an axis-squeeze and multirouting scale-adaptive fusion network (AMSFNet). In this network, a unidirectional multiscale coupling module (UMCM) is designed to enhance the object feature extraction capabilities and improve the accuracy of small object detection. Furthermore, utilizing the axis-squeeze and detail enhancement approach, we construct a squeeze-enhanced axial attention module that specifically targets background disruption, which can efficiently aggregate global and local information and reduce the impact of intricate background noise on the object. To improve the synergy between features of varying sizes and ensure that the model can allow for a compromise between detecting small and large targets simultaneously, a multirouting SAF approach is proposed, which combines fine-grained features with high-level features using multiple feed-forward connections. The proposed approach has been shown to be efficient by ablation and comparison experiments performed on three public benchmark datasets: RSOD, VisDrone, and DIOR. AMSFNet achieves a mean average precision (mAP) of 95.4%, 88.7%, and 36.6% on the RSOD, DIOR, and VisDrone datasets, respectively.