Joint information fusion and multi-scale network model for pedestrian detection

Abstract

The existing pedestrian detection suffers the low accuracy when the environment changes dramatically. In order to solve the problem, a pedestrian detection model combining information fusion and multi-scale detection is proposed. The model is composed of a retinex algorithm and an improved YOLOv3 algorithm. Retinex algorithm is selected as the preprocessing algorithm to improve the brightness and contrast of pedestrians. The model improves the YOLOv3 algorithm by adding multiple scale detections. The K-means is used to determine the number of optimal anchors and the aspect ratio. By testing on the standard data set, the mean average precision (mAP) of the joint detection model increases from the original 80.69–91.07%, and the recall increases from 65.22 to 87.48%. The comparative experiments show that the improved model performs good robustness and generalization ability on the problem of low pedestrian detection accuracy in complex environments.