# An Evaluation of Image Slicing and YOLO Architectures for Object Detection in UAV Images
## 1 Introduction  
Unmanned aerial vehicle (UAV) technology has made significant progress and is crucial for large-scale data collection and analysis. UAVs offer low-cost and highly efficient solutions in various fields, such as agriculture, environmental monitoring, security, and disaster management. UAVs are useful for quickly scanning large areas and capturing high-resolution images [1]. However, object detection in UAV images involves several technical challenges. The resolution of images, the complexity of backgrounds, the presence of small objects, and varying environmental conditions are critical factors that affect the accuracy of object detection algorithms. Especially in UAV images, due to these difficulties, object detection may not be achieved with the desired accuracy by traditional computer vision methods. Deep learning-based methods effectively detect small objects in low-resolution images and offer improved accuracy and performance in difficult detection tasks [2]. Deep learning methods have proved to be effective in object classification and localization. Recently, one-step detection algorithms such as YOLO (You Only Look Once) have become prominent by balancing speed and accuracy in object detection [3]. The YOLO algorithm provides a fast and efficient solution by classifying and localizing objects in a single step. However, detecting small objects in UAV images requires further improvement of existing models. In particular, problems such as low image resolution, tiny objects, and blending with the background negatively affect the detection accuracy [4].  
The YOLO algorithm provides a fast and efficient solution by classifying and localizing objects in a single step. However, researchers are still interested in improving models fordetecting small objects in UAV images. In particular, problems such as low image resolution, small objects, and background mixing reduce detection accuracy [4].  
In the previous study, the performance of the Image Slicing Algorithm (ISA) was examined using a fixed slice size [5]. Determining the slice size of images is critical for the performance of ISA, which is based on overlapping slices. The ISA algorithm slices images according to the input dimensions of the model to improve the performance of object detection algorithms. However, YOLO models usually support input dimensions such as 512, 640, or 1280 pixels for different image scales and hardware capabilities. In this study, the effect of the slice size on the performance was investigated to increase the object detection accuracy in UAV images. In addition, the balance between the real-time performance and the object detection performance was analyzed. The slice size directly affects the overlap amount and the integrity of the objects that are close to the edges. In the experiments, three different slicing sizes, 512, 640, and 1280 were determined, and the object detection performance of each size was evaluated separately for YOLOv7 [6], YOLOv8 [7], and YOLOv9 [8] models. The study aims to determine the slicing size and maximize the detection accuracy. In addition, the frame per second (FPS) values of the models were compared according to the selected slicing size. The contributions of the study can be summarized as follows:  
* The optimal slice size for achieving high accuracy and FPS in ISA performance was studied experimentally.
* Experimental studies were performed using data collected for the bottom view support of UAVs.
* Results were obtained using YOLOv7, YOLOv8, and YOLOv9 models for object detection based on the scenario designed.