# Pgds-YOLOv8s: An Improved YOLOv8s Model for Object Detection in Fisheye Images
## 2 Related Works  
### Cameras for Object Detection  
At present, object detection is a research hotspot in computer vision, and object detectors usually detect conventional life images taken with traditional perspective cameras. Perspective cameras are designed based on human vision and have a small viewing angle. [PERSON] et al. [16] presented the KITTI dataset, which captures 6 h of real-world traffic scenes using multiple sensor modalities such as high-resolution color and grayscale stereo cameras, a Velodyne 3D laser scanner, and a high-precision GPS/IMU inertial navigation system. The KITTI dataset provides images and object labels for automatic object detection research for driving. [PERSON] et al. [17] created the ONCE dataset, which consists of one million LiDAR scenes and seven million corresponding camera images selected from 144 driving hours. This dataset is used for a 3D object detection task in a self-driving scenario. In addition, the images captured by UAVs contain rich point-of-view information. For example, [PERSON] et al. [18] used a SkyReach BushCat light sport aircraft mounted with Canon 6D digital single-lens reflex cameras to collect 2101 images. The Aerial Elephant dataset contains 15,511 bush elephants in their natural African habitat. A baseline algorithm for elephant detection is also trained and tested to demonstrate the feasibility of the task. [PERSON] et al. [2] used a drone to collect 14,375 UAV aerial images. The WAID (Wildlife Aerial Images from Drone) dataset encompasses six wildlife species and multiple habitat types. This study brings new data to the field of UAV detection of wildlife. Meanwhile, the introduced SE-YOLO model effectively detects small objects in UAV images. Object detectors often detect regular images with smaller viewing angles. However, people's requirements for images are not only intuitive and clear but also more concerned about the completeness and comprehensiveness of image information. The imaging range of the fisheye lens can reach 180 deg-270 deg, and the binocular fisheye camera can acquire a 360-degree field of view. Fisheye cameras effectively fulfill the need for a wide viewing angle. The Oxford RobotCar Dataset [19] contains nearly 20 million images collected from vehicle-mounted cameras and LIDAR, GPS, and INS ground truths. The cameras include mainly the Point Grey Grasshopper2 monocular camera and the Point Grey Bumblebee XB3 trinocular stereo camera with a 180 deg fisheye lens. The Oxford RobotCar Dataset is used for research on localization and mapping of self-driving vehicles. [PERSON] et al. [20] presented the WoodScape dataset, which uses four fisheye cameras with 190 deg horizontal FOV, LiDAR, and other devices to acquire images. The WoodScape dataset provides labels for autonomous driving tasks, including semantic segmentation, monocular depth estimation, object detection, etc. However, the unique viewing angle of the fisheye camera causes problems such as significant image distortions, exaggerated relative sizes and distances of objects, and discontinuity of objects at the edges. [PERSON] et al. [21] proposed a pedestrian detection method for fisheye images in top view. The method generates multiple perspective views from the fisheye image and then detects the combined image of multiple perspective patches using existing detectors. [PERSON] et al. [22] proposed a shallow Concatenated Feature Pyramid Network (CFPN). The proposed concatenated block further reduces the number of convolutional layers. Also, the concatenated approach effectively preserves the spatial information of smaller objects at the end of the network. CFPN is better at detecting small vehicles in fisheye cameras in real-time traffic flow. [PERSON] et al. [23] proposed a multi-task network (MTL) to perform joint semantic segmentation, boundary prediction, and object detection on raw fisheye images. Two rotation-invariant object detection methods for fisheye images have also been explored, including YOLO-RotRect and YOLO-Circ.