Open Access

ARTICLE

Multiple Data Augmentation Strategy for Enhancing the Performance of YOLOv7 Object Detection Algorithm

Abdulghani M. Abdulghani, Mokhles M. Abdulghani, Wilbur L. Walters, Khalid H. Abed^*

Department of Electrical & Computer Engineering and Computer Science, Jackson State University, Jackson, 39217, USA

* Corresponding Author: Khalid H. Abed. Email:

Journal on Artificial Intelligence 2023, 5, 15-30. https://doi.org/10.32604/jai.2023.041341

Received 19 April 2023; Accepted 22 May 2023; Issue published 08 August 2023

Abstract

The object detection technique depends on various methods for duplicating the dataset without adding more images. Data augmentation is a popular method that assists deep neural networks in achieving better generalization performance and can be seen as a type of implicit regularization. This method is recommended in the case where the amount of high-quality data is limited, and gaining new examples is costly and time-consuming. In this paper, we trained YOLOv7 with a dataset that is part of the Open Images dataset that has 8,600 images with four classes (Car, Bus, Motorcycle, and Person). We used five different data augmentations techniques for duplicates and improvement of our dataset. The performance of the object detection algorithm was compared when using the proposed augmented dataset with a combination of two and three types of data augmentation with the result of the original data. The evaluation result for the augmented data gives a promising result for every object, and every kind of data augmentation gives a different improvement. The mAP@.5 of all classes was 76%, and F1-score was 74%. The proposed method increased the mAP@.5 value by +13% and F1-score by +10% for all objects.

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