# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 4 Experimental Results and Discussion
### Experimental Results and Discussion
#### 4.4.3 Results on the DIOR Dataset  
We conducted an evaluation of ProSAF-Net on the DIOR dataset and compared its performance with several state-of-the-art detectors, such as YOLOv3 [12], DFPN-YOLO [57], Faster R-CNN [10], CenterNet [7], ASSD [58], ABNet [16], CF2 PN [59], CSFFNet [60], MSA R-CNN [61], SESA-Net [52], TMAFNet [62], SRAF-Net [63], FSoD-Net [21], MSFC-Net [64], and CoF-Net [65]. As shown in Table 3, ProSAF-Net is the only method surpassing 76% mAP, achieving the best AP in three out of twenty categories, namely airport (85.6%), highway toll station (80.6%), and overpass (62.5%). While our approach attains the top AP in merely three categories, it ranks second in bridge, ship, and tennis court, demonstrating its effectiveness in detecting objects of varying scales. This is particularly important for large-scale datasets like DIOR. Furthermore, the ADIoU loss function is instrumental in driving this performance boost, as it dynamically adjusts the scale and position-related loss terms based on object size, improving the model's precision in pinpointing objects across various scales. Notably, a closer look at Table 3 reveals that CoF-Net also performs well, achieving the highest AP in three categories and ranking second in three others. Its overall mAP reaches 75.8%, which may be attributed to its mechanism of progressively enhancing feature representations and selecting stronger samples, thereby enabling the model to learn more discriminative features. However, our ProSAF-Net still outperforms CoF-Net by 0.3%, primarily due to the ADIoU loss function, which optimizes detection performance across different object scales. The detection outcomes of ProSAF-Net on the DIOR dataset are illustrated in Figure 8, showcasing diverse environments with intricate backgrounds and varying object scales. For example, when detecting storage tanks, both large and small instances are accurately identified with minimal precision discrepancy. This further demonstrates that our method can effectively detect objects under scale variations while maintaining high detection accuracy. In addition to its competitive detection performance, ProSAF-Net holds significant practical value in real-world applications. For instance, it can be employed in precision agriculture for pest or crop monitoring, in military reconnaissance for detecting vehicles or aircraft under complex backgrounds, and in tasks such as anti-counterfeiting or encryption that require high-resolution small object recognition. These applications benefit from the model's robustness to background noise and its effectiveness in handling small-scale targets. Furthermore, the three remote sensing datasets used in this study (RSOD, NWPU VHR-10, and DIOR) encompass diverse scene environments and varying object scales, such as urban areas, maritime regions, and mountainous terrains. As a result, the proposed model demonstrates strong performance across complex backgrounds, indicating its potential for deployment in a wide range of real-world domains.