Improved Mosaic: Algorithms for more Complex Images

Abstract

Data augmentation plays a vital role in deep learning, and image augmentation, as an important part of target detection and image classification, significantly improves the performance of the algorithm. The Mosaic data augmentation algorithm in YOLOv4 randomly selects 4 pictures from the train set and puts the contents of the 4 pictures into a synthetic picture that is directly used for training. This data augmentation method can improve the model’s recognition ability in complex backgrounds. In this paper, we improve the Mosaic data augmentation algorithm. After analyzing the synthesized picture area, it is divided into irregular grids, and a certain number of training set pictures are filled randomly, which further improves the synthesis ability and achieves a synthesis picture that can accommodate 6 and 9 training set pictures. After basic image processing methods such as zooming, flipping, and color gamut transformation, the model’s recognition ability under complex backgrounds is improved, and the accuracy of identifying small targets is improved. During the batch normalization operation, the data of 6 or 9 pictures can be calculated at the same time, which makes the model hyperparameter mini-batch need not be set very large, which reduces the GPU memory requirements.