# Pgds-YOLOv8s: An Improved YOLOv8s Model for Object Detection in Fisheye Images
## 6 Conclusions  
In this paper, the proposed PGDS-YOLOv8s model effectively improves the performance of distorted and discontinuous object detection for fisheye images. Firstly, two novel downsampling modules are proposed to demonstrate the superiority of detecting distorted and discontinuous objects. Among them, the MPGD module effectively extracts essential feature information about the distorted and discontinuous objects, and the APGD module can acquire the global field of view and reduce the feature loss of distorted and discontinuous objects. The two downsampling modules expand the sensing field to acquire more feature information and to construct a more lightweight network. Furthermore, the proposed C2 fs module acquires richer gradient flow information about features through the interdependence of SE block modeling channels. Meanwhile, the SPPFSE module is an SE module added after the SPPF module. The SPPFSE module can perceive the rich semantic information more efficiently to improve the whole model's ability to detect distorted and discontinuous objects in fisheye images.  
On the VOC-360 dataset, the proposed PGDS-YOLOv8s model obtains the best detection compared with YOLOv3-tiny, YOLOv5s, YOLOX-s, YOLOX-m, YOLOv6-S, YOLOv6-M, YOLOv7-tiny, and YOLOv8m. In addition, the improved model on the UAV-360 dataset achieves 89.0% for mAP@ 0.5 and 60.5% for mAP@ 0.5:0.95. Meanwhile, the detection results of our proposed method on the UAV-360 dataset all outperform other object detection methods for fisheye images. Furthermore, the PGDS-YOLOv8s model is used on the MS-COCO 2017 dataset for detection, and the AP is improved by 1.4%, AP\({}_{50}\) by 1.7%, AP\({}_{75}\) by 1.2%, AP\({}_{5}\) by 2.5%, AP\({}_{M}\) by 1.1%, and AP\({}_{L}\) by 1.7%. The proposed model also works well for conventional image detection, which proves its broad capability. In the future of computer vision, the increasing need for wide-angle imaging means that fisheye cameras will be widely studied and used. Therefore, the next part of this paper is dedicated to further investigating how to improve the lightweight characteristics of the model and the performance of the fisheye camera for object detection in real application scenarios.  
Conceptualization, D.Y., J.Z. and T.S.; methodology, D.Y., J.Z. and T.S.; software, J.Z., D.Y. and T.S.; validation, J.Z., D.Y. and T.S.; formal analysis, D.Y., J.Z. and T.S.; investigation, D.Y., J.Z. and T.S.; resources, D.Y. and T.S.; data curation, J.Z., X.Z. and Y.S.; writing--original draft preparation, D.Y., J.Z. and T.S.; writing--review and editing, D.Y., J.Z. and T.S.; visualization, J.Z., X.Z. and Y.S.; supervision, D.Y. and T.S.; project administration, D.Y. and T.S.; funding acquisition, D.Y. and T.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by Natural Science Foundation of Chongqing (CSTB 2022 NSCQ-MSX1200), in part by the Science and Technology Research Program of Chongqing Municipal Education Commission (KJZD-M202300502 and KJQN202200537), and in part by Chongqing Normal University Ph.D. Start-up Fund (21 XLB035).  
Not applicable.  
The data used to support the findings of this study were obtained from ([PERSON] et al., 2023).  
The authors thank the editors and anonymous reviewers for their critical comments and suggestions for improving the manuscript.  
The authors declare no conflict of interest.