# OrientedDiffDet: Diffusion Model for Oriented Object Detection in Aerial Images
## 4 Experiments
### Ablation Studies  
We conduct ablation experiments on DOTA to study OrientedDiffDet in detail to verify the effectiveness of the oriented box representation, feature alignment and accelerated diffusion. All experiments use ResNet-50 with FPN as the backbone and 2000 boxes for training and inference without further specification.  
The experimental results presented in Table 3 highlight the effectiveness of our model in achieving the accurate localization and recognition of objects as evidenced by the significant increase in mAP (+5.76%). It is worth noting that the inclusion of oriented box representation and feature alignment individually also leads to performance improvements, with an increase of 2.73 points and 1.01 points in mAP, respectively. This underscores the superior performance of the two proposed modules. It is important to mention that without accelerated diffusion, our OrientedDiffDet model exhibits slower inference speed compared to the original DiffusionDet (11.9 s vs. 10.8 s). In contrast, the accelerated model achieves a reduction in inference time (\(-0.6\) s). This trade-off between speed and accuracy highlights the significance of our accelerated diffusion technique in improving the overall efficiency of the object detector.  
To better understand OrientedDiffDet, we divided its main components into three aspects: box-oriented representation, feature alignment, and accelerated diffusion. First, box-oriented representation as a key element in the detection task contributes to improving detection accuracy. By dividing the target object into multiple oriented box, we can capture the object shape and pose more precisely. This representation helps to distinguish between similar objects and thus improve the detection performance. Second, feature alignment is used for spatially invariant feature extraction and can effectively improve target classification and positional regression. This alignment helps to preserve the details in the image and improve the performance of the detector. Finally, the accelerated diffusion module is important in improving the detection speed. OrientedDiffDet accelerated diffusion has a fast inference speed while maintaining high detection accuracy. Ablation experiments performed on the DOTA dataset demonstrated the effectiveness of OrientedDiffDet. We evaluated the effect of three parts, box-oriented representation, feature alignment, and accelerated diffusion, on detection performance separately. The experimental results show that all three parts positively affect the detection performance. In particular, for the box-oriented representation and feature alignment, they play a key role in improving the detection accuracy. The accelerated diffusion module improves the inference speed while ensuring the detection performance. The experimental results show that the combination of these three key techniques enables our model to achieve higher accuracy in locating and identifying objects, while improving the overall efficiency without sacrificing too much inference speed. This provides useful lessons and implications for future object detection studies.