论文标题
基于Yolov3和迁移学习的红外图像行人目标检测
Infrared image pedestrian target detection based on Yolov3 and migration learning
论文作者
论文摘要
随着红外夜视车辆援助系统在自动驾驶中的逐步应用，行人收集的红外图像的准确性逐渐改善。在本文中，迁移学习方法用于应用Yolov3模型来实现红外图像中的行人目标检测。目标检测模型Yolov3迁移到CVC红外行人数据集，DIOU损失用于替换原始Yolo模型的损失函数，以测试不同的超级参数以获得最佳的迁移学习效果。实验结果表明，在CVC数据集的行人检测任务中，Yolov3模型的平均准确度（AP）达到96.35％，Diou-Yolov3模型的平均准确性（AP）为72.14％，但后者的损失曲线融合速度更快。可以通过比较这两个模型来获得迁移学习的效果。
With the gradual application of infrared night vision vehicle assistance system in automatic driving, the accuracy of the collected infrared images of pedestrians is gradually improved. In this paper, the migration learning method is used to apply YOLOv3 model to realize pedestrian target detection in infrared images. The target detection model YOLOv3 is migrated to the CVC infrared pedestrian data set, and Diou loss is used to replace the loss function of the original YOLO model to test different super parameters to obtain the best migration learning effect. The experimental results show that in the pedestrian detection task of CVC data set, the average accuracy (AP) of Yolov3 model reaches 96.35%, and that of Diou-Yolov3 model is 72.14%, but the latter has a faster convergence rate of loss curve. The effect of migration learning can be obtained by comparing the two models.
