Analysis of deep learning frameworks for object detection in motion

Article type: Research Article

Authors: Gururaj, Vaishnavi | Ramesh, Shriya Varada | Satheesh, Sanjana | Kodipalli, Ashwini^* | Thimmaraju, Kusuma

Affiliations: Department of Artificial Intelligence and Data Science, Global Academy of Technology, Bengaluru, Karnataka, India

Correspondence: [*] Corresponding author: K. Ashwini, Department of Artificial Intelligence and Data Science, Global Academy of Technology, Bengaluru, Karnataka, India. E-mail: dr.ashwini.k@gat.ac.in.

Abstract: Object detection and recognition is a computer vision technology and is considered as one of the challenging tasks in the field of computer vision. Many approaches for detection have been proposed in the past. AIM: This paper is mainly aiming to discuss the existing detection and classification techniques of Deep Convolutional Neural Networks (CNN) with an importance placed on highlighting the training and accuracy of the different CNN models. METHODS: In the proposed work, Faster RCNN, YOLO and SSD are used to detect helmets. OUTCOME: The survey says MobileNets has higher accuracy when compared to VGG16, VGG19 and Inception V3 and is therefore chosen to be used with SSD. The impact of the differences in the amount of training of each algorithm is highlighted which helps understand the advantages and disadvantages of each algorithm and deduce the most suitable.

Keywords: Object detection, object classification, deep convolutional neural networks, RCNN, YOLO, SSD

DOI: 10.3233/KES-220002

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 26, no. 1, pp. 7-16, 2022