Comparative analysis of deep learning image detection algorithms

• Main Authors: Shrey Srivastava, Amit Vishvas Divekar, Chandu Anilkumar, Ishika Naik, Ved Kulkarni, V. Pattabiraman
• Format: Article
• Language: English
• Published: SpringerOpen 2021-05-01
• Series: Journal of Big Data
• Subjects: Object detection; FRCNN; YOLO-v3; SSD; COCO dataset
• Online Access: https://doi.org/10.1186/s40537-021-00434-w

Abstract A computer views all kinds of visual media as an array of numerical values. As a consequence of this approach, they require image processing algorithms to inspect contents of images. This project compares 3 major image processing algorithms: Single Shot Detection (SSD), Faster Region based Convolutional Neural Networks (Faster R-CNN), and You Only Look Once (YOLO) to find the fastest and most efficient of three. In this comparative analysis, using the Microsoft COCO (Common Object in Context) dataset, the performance of these three algorithms is evaluated and their strengths and limitations are analysed based on parameters such as accuracy, precision and F1 score. From the results of the analysis, it can be concluded that the suitability of any of the algorithms over the other two is dictated to a great extent by the use cases they are applied in. In an identical testing environment, YOLO-v3 outperforms SSD and Faster R-CNN, making it the best of the three algorithms.