Infrared image pedestrian target detection based on Yolov3 and migration learning

TL;DR

This paper applies migration learning to adapt the YOLOv3 model for infrared pedestrian detection, achieving high accuracy and faster convergence, which enhances night vision vehicle assistance systems.

Contribution

It introduces a migration learning approach with Diou loss for infrared pedestrian detection, improving model adaptation and convergence speed.

Findings

YOLOv3 achieves 96.35% AP on CVC dataset.

Diou-YOLOv3 reaches 72.14% AP with faster convergence.

Migration learning effectively adapts models to infrared pedestrian detection.

Abstract

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.