# Pgds-YOLOv8s: An Improved YOLOv8s Model for Object Detection in Fisheye Images
## 3 Materials and Methods
### The Proposed Approach
#### 3.2.1 Novel Downsampling Modules  
Currently, the advanced lightweight YOLOv8s network is used to detect conventional images. However, when the YOLOv8s network detects fisheye images, the distorted and discontinuous features of objects in fisheye images make the results perform poorly. Conventional convolutional kernels have difficulty recognizing distorted and discontinuous objects, which severely loses the feature information of the objects. In particular, the original YOLOv8s model only uses one (\(3\times 3\) convolutional kernels with a step size of 2) CBS to sample feature information in the downsampling stage. It is difficult to extract the features of distorted and discontinuous objects, losing many feature information of fisheye image objects. Therefore, two novel downsampling modules are proposed to solve the feature loss problem of distorted and discontinuous objects. Among them, the MPGD module is the Max Pooling and Ghost's Downsampling module, which can effectively extract the key features of distorted and discontinuous objects in fisheye images. The APGD module is the Average Pooling and Ghost's Downsampling module, which can effectively obtain the global features and reduce the feature loss of distorted and discontinuous objects. Conventional convolution in the advanced lightweight YOLOv8s model has difficulty recognizing distorted and discontinuous objects in fisheye images. In particular, much feature information of distorted and discontinuous objects is lost in the downsampling stage. So, inspired by YOLOv7 [35] downsampling, pooling [40] is used to solve this problem. Pooling suppresses noise and reduces information redundancy. Max Pooling sparsifies the error, and Average Pooling equalizes the mistake. It effectively enhances the network's ability to extract distorted and discontinuous features in fisheye images. In addition, the pooling operation does not care about the specific location of the features and only abstracts the region's features. Features at the edges of the fisheye image are  
Figure 5: The network structure of PGDS-YOLOv8s. less likely to be ignored, which is very beneficial for object detection in fisheye images. Meanwhile, pooling is often used to perform downsampling operations on the feature map, which can increase the network sensing field and improve the model's ability to perceive objects in fisheye images. In addition, the pooling layer has no parameters and does not require learning. In addition, to further enhance the pooling module's understanding of contextual information and feature representations in fisheye images, the Ghost module [41] is added after the pooling module to extend the perceptual domain. This allows the model to efficiently acquire important information about the features of distorted and discontinuous objects. The Ghost module exploits the similarity of feature mappings and uses inexpensive linear operations to generate redundant feature mappings with key information. Compared to convolutional operations, the Ghost module effectively reduces the parameters and computational cost of the model, resulting in a more lightweight network model. First, the Ghost module generates half of the intrinsic feature maps using a \(1\times 1\) primary convolution. Then, for the intrinsic feature maps, cheap linear operations are performed on each channel to generate the other half of the ghost feature maps. The cheap linear operation uses a \(5\times 5\) convolution kernel to expand the receptive field. Finally, the two parts of the feature maps are concatenated to output the final feature maps. In addition, identity mapping is used to preserve the intrinsic feature maps. As shown in Figure 6, the regular convolutional layer and the Ghost module are illustrated to generate the same number of feature maps, respectively. The proposed MPGD module uses two branches for the downsampling operation to effectively extract and retain the feature information of the feature map. Figure 7 illustrates the structure of the MPGD module.