Improving Data Augmentation for YOLOv5 Using Enhanced Segment Anything Model

Abstract

As one of the state-of-the-art object detection algorithms, YOLOv5 relies heavily on the quality of the training dataset. In order to improve the detection accuracy and performance of YOLOv5 and to reduce its false positive and false negative rates, we propose to improve the Segment Anything Model (SAM) used for data augmentation. The feature maps and mask predictions generated by the SAM are used as auxiliary inputs for the Mask-to-Mask (M2M) module. The experimental results show that after processing the dataset with the improved Segment Anything Model, the detection performance of YOLOv5 is improved with 99.9% precision and 99.1% recall. The improved YOLOv5 model has a higher license plate recognition accuracy than the original detection model under strong snowfall conditions, and the incidence of false-negative and false-positive is greatly reduced. The enhanced model can meet the requirement of accurate real-time recognition of license plates under strong snowfall weather conditions.