# Pgds-YOLOv8s: An Improved YOLOv8s Model for Object Detection in Fisheye Images
## 4 Results
### Comparison with Several Advanced Methods on the UAV-360 Dataset  
Table 3 shows a comparison of the experimental results of the proposed method with the state-of-the-art object detection methods for fisheye images. Among them, [PERSON] et al. [44] proposed three multi-scale feature connection models for detecting fisheye images, including Long-Skip Concatenation Model (LCat), Short-Skip Concatenation Model (SCat), and Short-Long-Skip Concatenation Model (SLCat). In addition, the edge continuity distortion-aware block (ECDAB) [45] and SphereConv [46] are introduced into the YOLOv8s model for comparison experiments with the proposed model, respectively. ECDAB mitigates object discontinuities and distortions in fisheye images by recombining and segmenting features. In addition, SphereConv resolves the object edge discontinuities and distortions in fisheye images by resampling. Our proposed method employs MPGD and APGD modules in the downsampling stage to expand the receptive field, while the CZfs module is utilized to acquire rich feature gradient flow information further. The features of distorted and discontinuous objects in fisheye images are effectively preserved to obtain good detection results. The experimental results demonstrate that all the object detection methods for fisheye images on the UAV-360 dataset have good detection accuracy, and the detection performance of our proposed method outperforms all the others. Among them, the FPS of the YOLOv8s model with the introduction of the MPGD and APGD modules reaches 208. The PGDS-YOLOv8s model achieves 89.0% for [EMAIL_ADDRESS] and 59.9% for [EMAIL_ADDRESS]:0.95. In particular, the YOLOv8s model introducing the C2 fs and SPPFSE modules achieves 60.5% for [EMAIL_ADDRESS]:0.95. With the complex background in the equirectangular projected image, the CZfs and SPPFSE modules allow the model to focus more on the informative channel features, further enhancing the model's understanding of the contextual information in the equirectangular projected image. The method obtains the best accuracy for object detection on the UAV-360 dataset. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Methods** & **Size** & **Params (M)** & **GFLOPs** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS] (\%)** & **FPS** \\ \hline YOLOv8s & 640 & 11.14 & 28.7 & 59.4 & 35.3 & 286 \\ YOLOv3-tiny & 640 & 8.71 & 13.1 & 57.7 & 27.2 & 476 \\ YOLOv5s & 640 & 7.07 & 16.1 & 63.9 & 34.3 & 357 \\ YOLOv8-s & 640 & 8.95 & 26.8 & 73.2 & 52.4 & 193 \\ YOLOv2-m & 640 & 25.29 & 73.79 & 78.0 & 60.2 & 91 \\ YOLOv6-S & 640 & 18.51 & 45.2 & 77.4 & 58.9 & 154 \\ YOLOv6-M & 640 & 34.82 & 85.67 & 78.9 & 61.1 & 107 \\ YOLOv7-tiny & 640 & 6.06 & 13.3 & 67.9 & 45.4 & 588 \\ YOLOv7 & 640 & 37.29 & 105.4 & 80.6 & 61.9 & 182 \\ YOLOv8m & 640 & 25.87 & 79.1 & 66.6 & 42.3 & 145 \\ PGDS-YOLOv8s & 640 & 10.79 & 28.5 & 79.2 & 62.8 & 250 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with several advanced models on the VOC-360 dataset. Figure 12 shows the test results for some equirectangular projection images in the UAV-360 test set. Images captured by the fisheye camera show that objects closer to the lens are larger and significantly distorted, while objects farther away from the lens are smaller. In the equirectangular projection images, objects at the poles of the image are particularly distorted, and objects located at the left and right edges of the image are discontinuous. The original YOLOv8 model does not perform well in dealing with these problems. For example, in the first scene of the first row, the detection accuracy of discontinuous buildings on the left and right sides of the equirectangular projection image is low. In the first scene of the first row, the flagpole platform is incorrectly detected as a car, and some of the vehicles cannot be recognized. In the first scene of the fourth row, a person is incorrectly detected as a motorbike. Compared with the original YOLOv8 model, the PGDS-YOLOv8s model can accurately recognize more objects and has higher object detection accuracy.