基于改进YOLOv3的智慧足球场行人检测

由于足球比赛场景中密集人群、移动小目标居多, YOLOv3算法存在检测精确度较低且模型参数量较大等问题, 使其无法部署在资源算力有限的移动设备上, 本文提出了一种基于改进YOLOv3的行人检测方法, 将Darknet-53主干特征提取网络替换为更加高效且轻量化的GhostNet网络; 同时选取了4个尺度的检测分支层并采用K-means++算法改善anchor box的聚类效果; 添加空间金字塔池化对输入图像实现相同大小的输出; 提出CIoU损失函数来计算目标定位损失值; 添加heatmap热力图可视化并在训练中使用Mosaic数据增强. 实验结果表明, YOLOv3-GhostNet在VOC融合数据集上mAP达到90.97%的同时相比YOLOv3算法提高了1.75%, 参数量减少了约81.4%且实时检测速率提高了约1.5倍, 在小型移动设备上表现出不错的检测效果.

Pedestrian Detection in Intelligent Football Field Based on Improved YOLOv3

Football match scenes are featured with dense crowds and many mobile targets, and YOLOv3 algorithm has low detection accuracy and requires massive model parameters, which makes it unable to be deployed on mobile devices with limited computing power. In view of these problems, this study proposes a pedestrian detection method based on improved YOLOv3. Specifically, the study replaces the Darknet-53 backbone feature extraction network with a more efficient and lightweight GhostNet network, selects detection branch layers with four scales, and adopts the K-means++ algorithm to improve the clustering effect of the anchor box. Furthermore, the study adds spatial pyramid pooling to achieve an output with the same size as the input image, puts forward the CIoU loss function to calculate the loss value of target positioning, adds heatmap visualization, and uses Mosaic data enhancement in training. The experimental results show that YOLOv3-GhostNet achieves a mAP of 90.97% on the VOC fusion dataset, with an improvement of 1.75% compared with the YOLOv3 algorithm. In addition, it reduces the number of parameters by about 81.4% and increases the real-time detection rate by about 1.5 times, which shows a positive detection effect on small mobile devices.