Pedestrian Detection in Fisheye Images Based on Improved YOLOv8 Algorithm
In view of the problems of inaccurate positioning and insufficient detection accuracy in pedestrian detection in fisheye images in existing target detection algorithms, an improved YOLOv8 algorithm for fisheye image detection is proposed. This method designs the ProbIoU-r algorithm by adding angle p...
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2025-02-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/2404037.pdf |
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| author | ZHU Yumin, SUN Guangling, MIAO Fei |
| author_facet | ZHU Yumin, SUN Guangling, MIAO Fei |
| author_sort | ZHU Yumin, SUN Guangling, MIAO Fei |
| collection | DOAJ |
| description | In view of the problems of inaccurate positioning and insufficient detection accuracy in pedestrian detection in fisheye images in existing target detection algorithms, an improved YOLOv8 algorithm for fisheye image detection is proposed. This method designs the ProbIoU-r algorithm by adding angle parameters, uses the scaling factor to adjust the impact of angle difference on the loss, and enhances the model’s attention to the angle offset of the bounding box in gradient calculation, solving the problems of inaccurate positioning of the original IoU in rotated target detection and poor bounding box fitting effect, so that the YOLOv8 network model has better ability to perceive rotated targets. In order to improve the model’s feature extraction ability for distorted targets in fisheye images and improve detection accuracy, a Parnet-gcs module with multi-scale convolution and attention mechanism as branches is proposed. The feature information of different scales is extracted through DWConv with different convolution kernels, and the CA and SA modules are combined to enhance the model’s feature expression ability. The experiment uses the public fisheye image dataset WEPDTOF. The improved algorithm increases the detection accuracy mAP0.50:0.95 by 2.3 percentage points compared with the original YOLOv8s; the number of parameters is reduced by 38.8% compared with the YOLOv8m algorithm, and the accuracy mAP0.50:0.95 is also 0.5 percentage points higher, indicating that the improved algorithm based on YOLOv8s is better suitable for pedestrian detection tasks in fisheye images. |
| format | Article |
| id | doaj-art-425f38dcdc5049cbb8fa852014589bb9 |
| institution | OA Journals |
| issn | 1673-9418 |
| language | zho |
| publishDate | 2025-02-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj-art-425f38dcdc5049cbb8fa852014589bb92025-08-20T02:16:14ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182025-02-0119244345310.3778/j.issn.1673-9418.2404037Pedestrian Detection in Fisheye Images Based on Improved YOLOv8 AlgorithmZHU Yumin, SUN Guangling, MIAO Fei01. School of Electronics and Information Engineering, Anhui Jianzhu University, Hefei 230601, China 2. Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei University of Technology, Hefei 230009, ChinaIn view of the problems of inaccurate positioning and insufficient detection accuracy in pedestrian detection in fisheye images in existing target detection algorithms, an improved YOLOv8 algorithm for fisheye image detection is proposed. This method designs the ProbIoU-r algorithm by adding angle parameters, uses the scaling factor to adjust the impact of angle difference on the loss, and enhances the model’s attention to the angle offset of the bounding box in gradient calculation, solving the problems of inaccurate positioning of the original IoU in rotated target detection and poor bounding box fitting effect, so that the YOLOv8 network model has better ability to perceive rotated targets. In order to improve the model’s feature extraction ability for distorted targets in fisheye images and improve detection accuracy, a Parnet-gcs module with multi-scale convolution and attention mechanism as branches is proposed. The feature information of different scales is extracted through DWConv with different convolution kernels, and the CA and SA modules are combined to enhance the model’s feature expression ability. The experiment uses the public fisheye image dataset WEPDTOF. The improved algorithm increases the detection accuracy mAP0.50:0.95 by 2.3 percentage points compared with the original YOLOv8s; the number of parameters is reduced by 38.8% compared with the YOLOv8m algorithm, and the accuracy mAP0.50:0.95 is also 0.5 percentage points higher, indicating that the improved algorithm based on YOLOv8s is better suitable for pedestrian detection tasks in fisheye images.http://fcst.ceaj.org/fileup/1673-9418/PDF/2404037.pdfobject detection; yolov8; attention mechanism; fisheye image |
| spellingShingle | ZHU Yumin, SUN Guangling, MIAO Fei Pedestrian Detection in Fisheye Images Based on Improved YOLOv8 Algorithm Jisuanji kexue yu tansuo object detection; yolov8; attention mechanism; fisheye image |
| title | Pedestrian Detection in Fisheye Images Based on Improved YOLOv8 Algorithm |
| title_full | Pedestrian Detection in Fisheye Images Based on Improved YOLOv8 Algorithm |
| title_fullStr | Pedestrian Detection in Fisheye Images Based on Improved YOLOv8 Algorithm |
| title_full_unstemmed | Pedestrian Detection in Fisheye Images Based on Improved YOLOv8 Algorithm |
| title_short | Pedestrian Detection in Fisheye Images Based on Improved YOLOv8 Algorithm |
| title_sort | pedestrian detection in fisheye images based on improved yolov8 algorithm |
| topic | object detection; yolov8; attention mechanism; fisheye image |
| url | http://fcst.ceaj.org/fileup/1673-9418/PDF/2404037.pdf |
| work_keys_str_mv | AT zhuyuminsunguanglingmiaofei pedestriandetectioninfisheyeimagesbasedonimprovedyolov8algorithm |