# BA-YOLO for Object Detection in Satellite Remote Sensing Images
## 4 Experiments
### Experimental Results  
We tested BA-YOLO on the DOTA dataset and compared it with other representative models, as shown in Table 2. On the DOTA dataset, our method achieves an mAP of 0.722, which is 0.036 higher than that of YOLOv8. This result demonstrates the effectiveness of BA-YOLO in satellite image object detection. In order to better assess the model's performance, we plotted P-R curves for each category as shown in Figure 5, and calculated the area under the curve, known as AP (average precision). A higher AP value indicates better detection performance. Additionally, two crucial parameters for evaluating deep learning models are the Intersection-over-Union (IoU) threshold and the confidence threshold. Next, in Figure 6, we separately computed the confusion matrix for BA-YOLO on the DOTA test dataset using an IoU threshold of 0.5 and a confidence threshold of 0.25. The confusion matrix is a tool for summarizing and visualizing the predictions of a classification problem. It counts the number of correct and incorrect predictions, categorizes them by each class, and illustrates the model's performance on different classes, including potential sources of confusion. As shown in Figure 6. The confusion matrix visually presents the classification results for each category. Each row represents the predicted categories by the model, each column represents the actual categories, and the data on the diagonal line represent the proportion of correctly classified categories. The confusion matrix helps us understand how the model performs on different classes, including which classes are prone to confusion or misclassification, thus aiding in further analysis and model performance improvement. The high false negatives (FNs) for the container crane category in the DOTA dataset can be attributed to the significantly lower number of training samples available for this category compared to other classes. The lack of training samples leads to limited feature extraction, resulting in a high FN rate. The high FN rate for the container crane category implies that a majority of objects belonging to this category are being missed by the model, and, consequently, the corresponding average precision (AP) for this category is much lower compared to others. On the other hand, it is evident that the category of small vehicles is prone to being missed. This is because, although there are sufficient training samples for small vehicles in the dataset, they are considered tiny objects with significant variations in scale, size, and orientation. Additionally, small vehicles in the images may \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **P** & **R** & **mAP** \\ \hline Faster R-CNN & 0.710 & 0.594 & 0.631 \\ RetinaNet & 0.714 & 0.585 & 0.622 \\ SSD & 0.696 & 0.522 & 0.561 \\ YOLOv3 & 0.715 & 0.546 & 0.587 \\ YOLOv5 & 0.760 & 0.601 & 0.645 \\ TPH-YOLOv5 & 0.785 & 0.643 & 0.683 \\ SPH-YOLOv5 & 0.806 & 0.683 & 0.716 \\ YOLOv8 & 0.719 & 0.647 & 0.686 \\ BA-YOLO & 0.769 & 0.690 & 0.722 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Test results on the DOTA test dataset for different detection models. Figure 5: The P–R curve from the BA-YOLO results on the DOTA test set. be situated in dark shadows, obscured by buildings, or densely parked in parking lots, making their detection challenging in such environments. Figure 7 showcases some visual results from our BA-YOLO on the DOTA dataset. The detected bounding boxes are color-coded according to different categories. Figure 8 showcases some visual results from the YOLOv8 detection model on the DOTA dataset. The confidence number calculation method in Figures 7 and 8 is as follows:  
\[C_{i}=P_{r}(Class_{i}|Object)\times P_{r}(Object)\times IoU(pred,truth) \tag{9}\]  
The term \(P_{r}(Class_{i}|Object)\) represents the probability of each detected category, \(P_{r}(Object)\) indicates whether the center of the object falls within the grid cell, and \(IoU(pred,truth)\) is the Intersection-over-Union value between the predicted box and the ground truth.