  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.

Artificial Intelligence;Computer Vision and Pattern Recognition