Abstract
Deep learning has proven to be a vital technique over the last few decades based on its potential to manage a huge amount of data. It is an artificial intelligence function that mimics the functioning of the human brain in accessing information and generating patterns to be used in problem solving. Using its methodologies, complex computer vision tasks like object detection including facial recognition and image classification can achieve cutting-edge results. With the help of deep learning object detection algorithms, we can instantly recognize and localize objects of interest once we look at pictures and videos with a meaningful result. The primary objective of object detection is to mimic this intellectual ability or intelligence using a computer. Object detection can be accomplished using a variety of deep learning techniques like YOLO and R-CNN including FASTER R-CNN and FAST R-CNN, and all these techniques use convolutional neural networks (CNN). We implemented these deep learning techniques to object detection in this paper as well as analyzed the results to choose which one performs better with the highest accuracy and we found that YOLO works better among four of them. However, the performance of these algorithms varies according to the scenarios in which they are used.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Pathak AR et al, Assessment of object detection using deep convolutional neural networks. In: Intelligent computing and information and communication, advances in intelligent systems and computing, vol 673. Springer, Singapore
Ren S, et al (2016) Faster R-CNN: towards real-time object detection with region proposal networks. In: TPAMI, IEEE, pp 91–99
Ye T, et al. (2011) Real-time detection of traffic flow combining virtual detection-line and contour feature. In: Proceedings of 2011 international conference on transportation, mechanical, and electrical engineering (TMEE), pp 408–413
Chen XY, Xiang SM, Liu CL, Pan CH (2014) Vehicle detection in satellite images by hybrid deep convolutional neural networks. IEEE Geosci Remote Sens Lett
Bose C, Pathak S, Agarwal R, Tripathi V, Joshi K (2020) A computer vision based approach for the analysis of acuteness of garbage. In: Singh M, Gupta P, Tyagi V, Flusser J, Ören T, Valentino G (eds) Advances in computing and data sciences. ICACDS 2020. Communications in computer and information science, vol 1244. Springer, Singapore. https://doi.org/10.1007/978-981-15-6634-9_1
Tomar A, Kumar S, Pant B et al (2021) Dynamic kernel CNN-LR model for people counting. Appl Intell. https://doi.org/10.1007/s10489-021-02375-6
Kim CE, Oghaz MMD, Fajtl J, Argyriou V, Remagnino P (2018) A comparison of embedded deep learning methods for person detection. arXiv preprint arXiv:1812.03451
Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2019) SSD: single shot multibox detector. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics)
Xi X, Yu Z, Zhan Z, Tian C, Yin Y (2019) Multi-task cost-sensitive-convolutional neural network for car detection. IEEE Access 1–1
Analytics Vidhya (2018) Retrieved on 26 March 2022 from https://www.analyticsvidhya.com/blog/2018/10/a-step-by-step-introduction-to-the-basic-object-detection-algorithms-part-1/
Pyimagesearch Retrieved on 26 March 2022 from https://929687.smushcdn.com/2633864/wpcontent/uploads/2017/09/example06_result.jpg? lossy=1&strip=1&webp=1
Pyimagesearch Retrieved on 26 March 2022 from https://929687.smushcdn.com/2633864/wpcontent/uploads/2018/11/yolo_living_room_out put.jpg?lossy=1&strip=1&webp=1
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kansal, V., Jain, U., Pant, B., Kotiyal, A. (2023). Comparative Analysis of Convolutional Neural Network in Object Detection. In: Tuba, M., Akashe, S., Joshi, A. (eds) ICT Infrastructure and Computing. Lecture Notes in Networks and Systems, vol 520. Springer, Singapore. https://doi.org/10.1007/978-981-19-5331-6_10
Download citation
DOI: https://doi.org/10.1007/978-981-19-5331-6_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-5330-9
Online ISBN: 978-981-19-5331-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)