Due to object detection's close relationship with video analysis and image understanding, it has attracted much research
attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures.
Their performance easily stagnates by constructing complex ensembles that combine multiple low-level image features with highlevel
context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which
can learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These
models behave differently in network architecture, training strategy, and optimization function, etc. In this paper, we provide a
review of deep learning-based object detection frameworks. Our review begins with a brief introduction to the history of deep
learning and its representative tool, namely Convolutional Neural Network (CNN). Then we focus on typical generic object
detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct
specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object
detection, face detection, and pedestrian detection. Experimental analyses are also provided to compare various methods and draw
some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in
both object detection and relevant neural network-based learning systems.
Published In:IJCSN Journal Volume 9, Issue 2
Date of Publication : April 2020
Pages : 93-100
Figures :06
Tables : --
Sheetal Singh Bhandari :
Department of Information Technology, Excelsior Education Society's
K.C. College of Engineering & Management Studies & Research, Thane, Maharashtra, India.
Shashank Dhoke :
Department of Information Technology, Excelsior Education Society's
K.C. College of Engineering & Management Studies & Research, Thane, Maharashtra, India.
Omkar Sarang :
Department of Information Technology, Excelsior Education Society's
K.C. College of Engineering & Management Studies & Research, Thane, Maharashtra, India.
Thus, we have learned:
. To create a system that will track and monitor
the scene.
. Detection of objects using trained models.
. Real-time detection and recognition of any
object instantly.
. Includes facial detection and people counting
where people tend to get lost easily.
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