Open AccessObject Detection Techniques based on Deep Learning: A Review
Author(s) -
Utkarsh Namdev,
Shikha Agrawal,
Rajeev Pandey
Publication year - 2022
Publication title -
computer science and engineering : an international journal
Language(s) - English
Resource type - Journals
eISSN - 2231-3583
pISSN - 2231-329X
DOI - 10.5121/cseij.2022.12113
Subject(s) - computer science , object detection , artificial intelligence , object class detection , pedestrian detection , cognitive neuroscience of visual object recognition , computer vision , object (grammar) , robotics , deep learning , image processing , viola–jones object detection framework , identification (biology) , facial recognition system , pedestrian , variety (cybernetics) , face detection , pattern recognition (psychology) , image (mathematics) , robot , engineering , transport engineering , botany , biology
Object detection is a computer technique that searches digital images and videos for occurrences of meaningful subjects in particular categories (such as people, buildings, and automobiles). It is related to computer vision and image processing. Two well-studied aspects of identification are facial and pedestrian detection. Object detection is useful in a wide range of visual recognition tasks, including image retrieval and video monitoring. The object detection algorithm has been improved many times to improve the performance in terms of speed and accuracy. “Due to the tireless efforts of many researchers, deep learning algorithms are rapidly improving their object detection performance. Pedestrian detection, medical imaging, robotics, self-driving cars, face recognition and other popular applications have reduced labor in many areas.” It is used in a wide variety of industries, with applications range from individual safeguarding to business productivity. It is a fundamental component of driver assist systems and driverless cars, which allows automobiles to identify driving lanes and pedestrians to avoid any accidents.