# Pgds-YOLOv8s: An Improved YOLOv8s Model for Object Detection in Fisheye Images
## 1 Introduction  
Object detection is an important research direction in computer vision, and current object detectors usually detect conventional images with small viewpoints. With the continuous development of computer vision technology, the imaging range of captured images needs to be broader. Unmanned aerial vehicles (UAVs) at a higher altitude can access a richer range of information than ground-based shots [1, 2]. The UAV expands the viewing angle by raising the height, and at the same time, the overall object within the image becomes smaller, which is difficult for current detectors to detect. Moreover, the fisheye lens, which has a viewing angle of 180 deg to 270 deg, has by far the largest viewing angle and can acquire rich visual information. It makes the fisheye camera have a wide range of applications, such as unmanned aerial vehicles [3, 4, 5, 6], autonomous driving [7, 8], robotics [9, 10, 11], and so on [12, 13, 14, 15]. In this paper, instead of expanding the visual rangeby elevating the height of the UAV, a UAV-mounted fisheye camera is used to obtain a 360\({}^{\circ}\) view without blind spots. The combination of a UAV and a fisheye camera makes the information acquisition more comprehensive, which is an important research area in the development of computer vision. The combination of a UAV and a fisheye camera can obtain information about the field of view without blind spots. Therefore, a UAV equipped with a fisheye camera plays a vital role in object detection, environmental monitoring, agricultural inspection, urban planning, disaster prevention and relief, etc. For example, [PERSON] et al. [3] used a 360-degree camera on a UAV to obtain a blind-free field of view, and this collocation can play a significant advantage in forest fire monitoring. The captured equirectangular images are first converted to stereoscopic images. Then, the DeepLab V3+ networks are utilized to segment the flames and smoke in the images, and a post-validation adaptive method is utilized to reduce the rate of false positives. This approach effectively achieves the detection and localization of fire regions and can play an active role in early fire detection. Furthermore, [PERSON] et al. [4] used panoramic images captured by UAVs to address the cost and safety concerns associated with the structural health assessment of infrastructure. Multiple-projection methods are proposed to address the effects of panoramic image distortion. Then, deep neural networks are used to detect the damage to multiple steel surfaces in the 360\({}^{\circ}\) panoramic image. In addition, [PERSON] et al. [5] used two fisheye cameras to obtain an omnidirectional visual range for aerial robots, which is beneficial for safe navigation in complex environments. Meanwhile, a dual-fisheye visual-inertial system (VINS) is proposed, which uses two fisheye cameras and an inertial measurement unit (IMU) to realize spherical omnidirectional sensing. The dual-fisheye omnidirectional visual-inertial state estimator is externally calibrated to optimize the fisheye image distortion problem. [PERSON] et al. [6] constructed an autonomous landing system for UAVs that can land automatically in GPS-denied environments. The system combines a fisheye camera with a wide field of view and a stereo camera with depth imaging to obtain rich visual information, forming a hybrid camera array that is easier to pinpoint. In particular, the system employs YOLOv3 to directly detect objects in a fisheye image, robustly realizing autonomous landing on a moving unmanned ground vehicle (UGV). Most existing object detectors in computer vision often detect regular images with small viewing angles. This paper investigates and uses a fisheye camera to further explore object detection for wide-view angle images. Fisheye cameras have a wide angle of view, and a UAV equipped with a fisheye camera can obtain a blind-spot-free view. However, distorted and discontinuous objects appear in the captured fisheye images due to the unique viewing angle of fisheye cameras.