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With the gradual application of infrared night vision vehicle assistancesystem in automatic driving, the accuracy of the collected infrared images ofpedestrians is gradually improved. In this paper, the migration learning methodis used to apply YOLOv3 model to realize pedestrian target detection ininfrared images. The target detection model YOLOv3 is migrated to the CVCinfrared pedestrian data set, and Diou loss is used to replace the lossfunction of the original YOLO model to test different super parameters toobtain the best migration learning effect. The experimental results show thatin the pedestrian detection task of CVC data set, the average accuracy (AP) ofYolov3 model reaches 96.35%, and that of Diou-Yolov3 model is 72.14%, but thelatter has a faster convergence rate of loss curve. The effect of migrationlearning can be obtained by comparing the two models.

Infrared image pedestrian target detection based on Yolov3 and migration learning