Dynamic Mosaic algorithm for data augmentation

Abstract

Convolutional Neural Networks (CNNs) have achieved remarkable results in the computer vision field. However, the newly proposed network architecture has deeper network layers and more parameters, which is more prone to overfitting, resulting in reduced recognition accuracy of the CNNs. To improve the recognition accuracy of the model of image recognition used in CNNs and overcome the problem of overfitting, this paper proposes an improved data augmentation approach based on mosaic algorithm, named Dynamic Mosaic algorithm, to solve the problem of the information waste caused by the gray background in mosaic images. This algorithm improves the original mosaic algorithm by adding a dynamic adjustment step that reduces the proportion of gray background in the mosaic image by dynamically increasing the number of spliced images. Moreover, to relieve the problem of network overfitting, also a Multi-Type Data Augmentation (MTDA) strategy, based on the Dynamic Mosaic algorithm, is introduced. The strategy divides the training samples into four parts, and each part uses different data augmentation operations to improve the information variance between the training samples, thereby preventing the network from overfitting. To evaluate the effectiveness of the Dynamic Mosaic algorithm and the MTDA strategy, we conducted a series of experiments on the Pascal VOC dataset and compared it with other state-of-the-art algorithms. The experimental results show that the Dynamic Mosaic algorithm and MTDA strategy can effectively improve the recognition accuracy of the model, and the recognition accuracy is better than other advanced algorithms.

Keywords

• data augmentation
• mosaic algorithm
• yolov5
• deep learning
• object detection