# Pgds-YOLOv8s: An Improved YOLOv8s Model for Object Detection in Fisheye Images
## 3 Materials and Methods
### The Proposed Approach  
Fisheye cameras have a wide viewing angle and can acquire rich perspective information. However, this leads to distorted and discontinuous objects in fisheye images. Therefore, this paper proposes a PGDS-YOLOv8s model based on the Novel Pooling and Ghost's Downsampling module and the SE module. The proposed PGDS-YOLOv8s model can effectively improve the detection performance of distorted and discontinuous objects in fisheye images. In addition, compared to the convolution of the downsampling stage of the original YOLOv8s, the two proposed Pooling and Ghost's Downsampling modules slightly reduce the parameters and computation of the model.  
YOLOv8s, a state-of-the-art lightweight object detection network, is used as the base model in this paper. However, for the specificity of objects in fisheye images, the YOLOv8s model performs poorly in detection performance. In particular, the original YOLOv8s model employs a (\(3\times 3\) convolutional kernel with a step size of 2) CBS module in the downsampling stage, which has difficulty in extracting features from distorted and discontinuous objects, thus losing a large amount of feature information. Therefore, two downsampling modules are proposed to solve the problem of feature loss for distorted and discontinuous objects. One of them is called the MPGD module, which is a downsampling module based on the Max Pooling and Ghost module, and the other is called the APGD module, which is a downsampling module based on the Average Pooling and Ghost module. The MPGD module efficiently extracts the key features of the distorted and discontinuous objects. The APGD module can obtain the global field of view, which reduces the feature loss of distorted and discontinuous objects. The two modules are used in the PGDS-YOLOv8s model to effectively improve the network's ability to recognize distorted and discontinuous objects in fisheye images. At the same time, compared to the CBS module of the original YOLOv8 model, the proposed MPGD and APGD modules reduce the parameters and computation of the model. In Backbone, the MPGD and APGD modules replace the (\(3\times 3\) convolutional kernel with a step size of 2) CBS modules. The MPGD and APGD modules alternate for the downsampling operation, using the MPGD module first and then the APGD module. The APGD module improves the characteristic of the MPGD module that only extracts the most significant features, and it also focuses on the global features to maximize the retention of the information of the feature map. Meanwhile, in Head, the MPGD module replaces the two (\(3\times 3\) convolution kernel with step size 2) CBS modules in the downsampling stage. Based on the features extracted from the backbone, the effective features in the fisheye image are further extracted. The MPGD and APGD modules have a large sensory field, which can effectively improve the performance of detecting distorted and discontinuous objects in the fisheye image.  
In addition, the C2f module in the original YOLOv8 model uses a gradient shunt connection to obtain rich gradient flow information. To further enhance the model's understanding of the contextual information of fisheye images, the C2 fs module is proposed. The C2 fs module is the SE attention mechanism added to each Bottleneck of the C2f module. The C2 fs module further enhances the ability to acquire channel features by retaining important feature information and suppressing unimportant feature information. Furthermore, the SPPFSE module is the addition of an SE layer after the Spatial Pyramid Pooling Fast (SPPF) module. It can use global information to selectively emphasize informative features, thereby improving the ability of the whole model to select and capture features. The proposed PGDS-YOLOv8s model is a lightweight network structure for fisheye images, which applies our proposed modules in the YOLOv8s model. Figure 5 shows the network structure of PGDS-YOLOv8s.