# Pgds-YOLOv8s: An Improved YOLOv8s Model for Object Detection in Fisheye Images
## 2 Related Works
### Object Detection Methods  
Deep-learning-based object detection frameworks can be categorized into two-stage object detection algorithms and single-stage object detection algorithms. The two-stage object detection algorithm consists of generating candidate regions and performing a classification process on them. Two-stage object detectors include R-CNN [25], Fast-RCNN [26], Mask R-CNN [27], etc. [PERSON] et al. [25] proposed the R-CNN. Object candidate regions are first generated on the image, then features are extracted using a convolutional neural network for the candidate regions, and a support vector machine classifier is applied for classification. Subsequently, [PERSON] et al. [26] proposed the Fast RCNN, which incorporates the ideas of SPP-net to improve R-CNN and introduces the pooling of regions of interest to unify the input size. Mask R-CNN [27] adds another parallel branch for pixel-level segmentation of object instances, which detects the objects and allows for objects to perform pixel-level segmentation. The two-stage object detection algorithm is more accurate. However, it is slower and still has obstacles in meeting the needs of complex object detection scenarios. The other is the single-stage object detection algorithm, which treats object detection as a one-time problem and quickly localizes and classifies objects end-to-end. Single-Shot MultiBox Detector (SSD) [28] is a classic single-stage object detection algorithm that guarantees detection speed while keeping the detection accuracy comparable to two-stage object detection algorithms. You Only Look Once (YOLO) [29] is an iconic algorithm for single-stage object detection that transforms the detection problem into a regression problem, but it is not highly effective in detecting small objects. YOLOv2 [30] uses Darknet-19 to extract object features and uses global average pooling and batch normalization to improve network convergence. YOLOv3 [31] employs a residual network module and uses Darknet-53 as a backbone to enhance object detection performance. YOLOv4 [32] uses Complete Intersection over Union (CIoU) loss for predictive frame filtering to improve the accuracy and robustness of the model. YOLOv5 [33] uses Feature Pyramid Network (FPN) and Pixel Aggregation Network (PANet) structures in the neck network, which is designed to be lightweight and faster. [PERSON] et al. [34] proposed YOLOv6, which injects a self-distillation strategy for classification and regression tasks. YOLOv7 [35] proposed E-ELAN, which does not change the original gradient path, uses group convolution to increase the cardinality of the added features, and combines different groups of features in a way that shuffles and merges the cardinality. Although these object detectors perform well, considering the characteristics of distorted and discontinuous objects in fisheye images and the balance between speed and accuracy, this paper uses the YOLOv8s model for improvement and experimentation. YOLOv8 [36] is a fast, accurate, robust, and lightweight network built on YOLOv5. The main ideas of the YOLOv8 algorithm are as follows. First, the C2f module is designed, which refers to the ideas of the C3 module in YOLOv5 and Efficient Layer Aggregation Networks (ELANs). It makes the YOLOv8 model lightweight and effectively improves the object detection accuracy of the model. Next, YOLOv8 compares with YOLOv5, which removes the objectness branch and keeps the classification and regression branches. The decoupling structure in the detection section separates the regression and classification, and the Distributive Focal Loss (DFL) is used as the regression loss, allowing the network to quickly focus on the location distribution close to the object location. Then, YOLOv8s uses the Anchor-Free method instead of the Anchor-Base method to correct the object positions. The Anchor-Free method predicts the distance from the center to the bounding box after locating the object's center.