# Pgds-YOLOv8s: An Improved YOLOv8s Model for Object Detection in Fisheye Images
## 5 Discussion  
Most existing object detectors often detect regular images with small viewing angles. To further explore the object detection of wide-view angle images, this paper uses images with wide-view angles captured by a fisheye camera mounted on a UAV. However, the  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Methods** & **Params (M)** & **GFLOPs** & **AP (\%)** & **AP\% (\%)** & **AP\% (\%)** & **AP\% (\%)** & **AP\% (\%)** & **AP\% (\%)** & **FPS** \\ \hline YOLOv8s & 11.16 & 28.7 & 42.1 & 58.4 & 45.9 & 22.9 & 46.7 & 58.0 & 303 \\ YOLOv8s + MPGD + APGD & 10.81 & 28.5 & 43.1 & 59.4 & 47.0 & 24.3 & 47.7 & 58.5 & 286 \\ YOLOv8s + CZfs + SPPFSE & 11.17 & 28.7 & 42.4 & 58.9 & 46.3 & 23.2 & 47.2 & 58.5 & 303 \\ PGDS-YOLOv8s & 10.81 & 28.5 & 43.5 & 60.1 & 47.1 & 25.4 & 47.8 & 59.7 & 294 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Improved models comparison on the MS-COCO 2017 dataset.  
Figure 12: Comparison of the test results of the two models on the UAV-360 test set. (**a**) YOLOv8s model; (**b**) PGDS-YOLOv8s model.  
unique viewing angle of the fisheye camera leads to problems such as object distortion and discontinuity of objects at the edges in fisheye images. This poses a significant challenge to advanced object detectors. Therefore, to solve these problems, this paper proposes a PSG-YOLOv8s model for object detection in fisheye images. The MPGD module effectively extracts key feature information of distorted and discontinuous objects. The APGD module acquires rich global features and reduces feature loss of distorted and discontinuous objects. In addition, the C2 fs module models the interdependence of the channels to acquire richer gradient flow information of the features. Subsequently, the SPPFSE module further improves the model's ability to capture features of distorted and discontinuous objects. The experimental results in this paper demonstrate that the proposed method can extract distorted and discontinuous features effectively, thus obtaining excellent detection performance. The proposed model is compared with some current state-of-the-art object detectors on the VOC-360 dataset. For fisheye images with a spherical viewing angle, the accuracy of the PGDS-YOLOv8s model is better than YOLOv3-tiny, YOLOv5s, YOLOX-s, YOLOX-m, YOLOv6-S, YOLOv6-M, YOLOv7-tiny, YOLOv8s, and YOLOv8m, except YOLOv7, which outperforms the PGDS-YOLOv8s model on [EMAIL_ADDRESS]. In addition, the UAV-360 dataset is an equirectangular projected image converted from a fisheye image captured by a fisheye camera mounted on the UAV. On the UAV-360 dataset, the proposed method performs better in the object detection of fisheye images than advanced methods for processing fisheye images, such as LCat, SCat, SLCat, ECDAB, and SphereConv. Among them, the C2 fs and SPPFSE modules make the model pay more attention to the information channel features and obtain the best detection accuracy. Meanwhile, to further validate the broad applicability of the proposed method, it is experimented on the MS-COCO 2017 dataset. Compared with the original YOLOv8s model, the PGDS-YOLOv8s model improves detection accuracy.  
Limitations. The MPGD and APGD modules effectively extract distorted and incomplete features of fisheye images during the downsampling stage. However, compared with the original YOLOv8s model, the proposed model, with the introduction of the MPGD and APGD modules, takes up slightly more memory during training, which affects the detection speed.