Pedestrian Detection in Fisheye Images Based on Improved YOLOv8 Algorithm

doi:10.3778/j.issn.1673-9418.2404037

Abstract

Abstract: 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.

Key words: object detection, YOLOv8, attention mechanism, fisheye image

摘要： 针对现有目标检测算法在鱼眼图像下行人检测中存在定位不准确、检测精度不足等问题，提出了一种面向鱼眼图像检测的YOLOv8改进算法。该方法通过加入角度参数，设计了ProbIoU-r算法，利用缩放因子调整角度差异对损失的影响，增强模型在梯度计算中对边界框角度偏移的关注，解决了原始IoU在旋转目标检测定位不精确、边界框拟合效果差等问题，使YOLOv8网络模型具有更好感知旋转目标的能力。为提高模型对鱼眼图像失真目标的特征提取能力同时提升检测精度，提出以多尺度卷积和注意力机制为分支的Parnet-gcs模块，通过不同卷积核的DWConv提取不同尺度的特征信息，并结合CA和SA模块以增强模型特征表达能力。实验采用公开的鱼眼图像数据集WEPDTOF，改进后算法相比原始YOLOv8s检测精度mAP0.50:0.95增加了2.3个百分点；相比YOLOv8m算法参数量减少了38.8%，同时精度mAP0.50:0.95也高出0.5个百分点，说明基于YOLOv8s改进后的算法能够更好适用于鱼眼图像下行人检测任务。

关键词: 目标检测, YOLOv8, 注意力机制, 鱼眼图像

ZHU Yumin, SUN Guangling, MIAO Fei. Pedestrian Detection in Fisheye Images Based on Improved YOLOv8 Algorithm[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(2): 443-453.

朱玉敏, 孙光灵, 缪飞. 基于改进YOLOv8算法的鱼眼图像下行人检测[J]. 计算机科学与探索, 2025, 19(2): 443-453.

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