关键词: 目标检测, YOLOv8, 注意力机制, 鱼眼图像

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 mAP.5:.95 by 2.3% compared with the original YOLOv8s; the number of parameters is reduced by 38.8% compared with the YOLOv8m algorithm, and the accuracy mAP.5:.95 is also 0.5% 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