# An Evaluation of Image Slicing and YOLO Architectures for Object Detection in UAV Images
## 2 Related Work  
[PERSON] et al. [9] proposed UAV-YOLOv8 to improve the detection accuracy of objects in UAV images. They improved the focus of the model on important information through the BiFormer attention mechanism and reduced the rate of missed objects by integrating deep and shallow features with the Focal FasterNet block. [PERSON] et al. [10] proposed a model based on YOLOv8n to enhance the accuracy of object detection in UAV images. They introduced three key modifications to the YOLOv8n architecture: the addition of new scaled sensing layers to improve object detection, the enhancement of the model's representativeness via conditional parameterization convolutions, and the implementation of a dynamic focusing mechanism known as Wise-IoU for box regression. [PERSON] and [PERSON] [11] proposed a YOLOv5s-based algorithm model to improve object detection performance in UAV images. Their proposed framework used a lightweight module called DCNCSPELAN4 for better feature extraction and integrated global and local information with a Vision Transformer block called CloFormer. With this model, they aimed to improve the detection of objects with multi-scale feature fusion. [PERSON] et al. [12] addressed issues such as small targets, multiple scales, and complex backgrounds that pose challenges for UAV images. In their work, they replaced the backbone network of YOLOv4 with MobileNetv3, regrouped the network with the K-means++ algorithm to improve the detection rate, and replaced the stride convolution in PANet with SPD-Conv to avoid further information loss. They used a \"copy-pasting\" data augmentation strategy for small targets and added the Focal loss function to the model to reduce the false detection rate in complex backgrounds. [PERSON] et al. [13] proposed a lightweight object detection model called YOLO-ERF for object detection in UAV images. YOLO-ERF aimed to preserve local details while increasing the detection area of convolutional kernels using an effective perceptual field (ERF) module. With this model, they aimed to offer better accuracy with fewer parameters by using a lightweight backbone network (ERFNet) and an improved PAN structure that reduces weight and parameters. They also designed a new detection head (ERF-Head) in the model to better detect objects on complex backgrounds. [PERSON] and [PERSON] [14] proposed a two-stage method to improve object detection in UAV images. In the first stage, they developed a method to detect small moving objects through background subtraction and object tracking. In the second stage, they applied YOLOv5-based object detection. In background subtraction, they used the Gaussian Mixture Model (GMM) to adapt to background changes, followed by object tracking with the Kalman filter. [PERSON] et al. [15] introduced YOLOv7-P, a lightweight and efficient algorithm to improve object detection in UAV images. Based on YOLOv7, this model offers several optimizations to detect objects better. First, they used partial convolution (PConv) to reduce unnecessary parameters and computation. Furthermore, an optimal combination of detection heads was proposed to improve the detection performance of small objects. In the last part of the network, they introduced a new convolution method called PConv-wide, which had a lighter structure than RepConv. Finally, they combined normalized Wasserstein distance loss with CIoU loss to improve the detection sensitivity of objects. [PERSON] et al. [5] proposed a multi-stage method for object detection in high-resolution images. Their method aimed to improve object detection using the ISA and Segment Anything Model (SAM). In the first stage, images were sliced using ISA, and the label accuracy of objects in these slices was checked with SAM. In the second stage, Super-Resolution Object Detection (SROD), a model combining Super-Resolution Generative Adversarial Network (SRGAN) and YOLO algorithms, was used to increase the resolution of the sliced images and perform object detection. [PERSON] et al.