Infrared image pedestrian target detection based on Yolov3 and migration learnin

Infrared image pedestrian target detection based on Yolov3 and migration learning

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.

基于Yolov3和迁移学习的红外图像行人目标检测。

随着红外夜视车辆辅助系统在自动驾驶中的逐步应用，所采集的行人红外图像的准确性逐渐提高。本文采用迁移学习方法，将YOLOv3模型应用于红外图像中行人目标的检测。.. 将目标检测模型YOLOv3迁移到CVC红外行人数据集，并使用Diou损失代替原始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模型的平均准确度为72.14％，但后者的损失曲线收敛速度更快。可以通过比较两个模型来获得迁移学习的效果。 （阅读更多）