# An Evaluation of Image Slicing and YOLO Architectures for Object Detection in UAV Images
## 5 Results and Discussion
The confusion matrix of this model on the test data is shown in Figure 8.  
In the study, when the confusion matrix of the model that obtained the highest mAP was examined, the UAI class obtained the best score. The values shown as the background class in the confusion matrices are the rate at which the object belonging to that class was present in the test data but could not be detected by the model, or a detection was made that did not belong to the class. As can be seen in the confusion matrix, the model's correct detection of the people class is relatively low compared to the other classes. The reason for this is that the people class is represented as small objects in aerial images. The precision-recall graph of the YOLOv7x model with the best performance is shown in Figure 9. The graph shows the classes' AP values and the model's overall mAP value. The model performance is promising for the amount of data used in the tests. The 1280 \(\times\) 1280 sliced images entered into the model without pixel loss gave a very successful performance. The application of ISA preprocessing improved the overall performance of the models. The lack of pixel loss is significant for the models to learn small objects better. Table 6 shows the overall performance of the models on the test data obtained from training with new images obtained from pre-processing according to the slicing sizes made in this study. When the overall performances are analyzed, it is seen that the best results are on the images obtained by slicing 1280 \(\times\) 1280. It is observed that as the overlap increases and the slicing size increases, the objects corresponding to the edges are better preserved, positively affecting the performance of the model. Figure 8: Confusion Matrix for YOLOv7x. According to the experimental results with different versions of YOLO, the slice size used significantly affects object detection performance. In particular, as the slice size increases, there is a significant increase in object detection accuracy. This improvement is mainly due to larger slice sizes, which allow the models to analyze the objects in the image in detail. However, the increase in slice size decreases the processing speed of the models and causes a decrease in FPS values. On the other hand, the FPS values obtained from experimental studies are generally sufficient for real-time applications. Especially, when working on high-resolution and complex datasets such as UAV images, keeping the slice size high depending on the hardware power will likely improve object detection performance. In such applications, it is important to determine the slice size based on the computational capabilities of the hardware to achieve a balanced performance that optimizes both accuracy and speed. As a result, in object detection studies conducted on challenging datasets such as UAV images, it is recommended that large slice sizes be preferred to increase the detection accuracy of the model. In addition, as long as the FPS values are sufficient within the limits of the hardware used, this approach also offers a practical solution for real-time applications.