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The YOLOv3 target detection algorithm is widely used in industry due to itshigh speed and high accuracy, but it has some limitations, such as the accuracydegradation of unbalanced datasets. The YOLOv3 target detection algorithm isbased on a Gaussian fuzzy data augmentation approach to pre-process the dataset and improve the YOLOv3 target detection algorithm. Through the efficientpre-processing, the confidence level of YOLOv3 is generally improved by0.01-0.02 without changing the recognition speed of YOLOv3, and the processedimages also perform better in image localization due to effective featurefusion, which is more in line with the requirement of recognition speed andaccuracy in production.

An improved helmet detection method for YOLOv3 on an unbalanced dataset