Investigations of object detection in images/videos using various deep learning techniques and embedded platforms-A comprehensive review
- Authors
- Murthy C.B.; Hashmi M.F.; Bokde N.D.; Geem Z.W.
- Issue Date
- May-2020
- Publisher
- MDPI AG
- Keywords
- Computer vision (CV); Convolutional neural network (CNN); Deep learning techniques; Graphics processing units (GPUs); Object detection
- Citation
- Applied Sciences (Switzerland), v.10, no.9
- Journal Title
- Applied Sciences (Switzerland)
- Volume
- 10
- Number
- 9
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/53433
- DOI
- 10.3390/app10093280
- ISSN
- 2076-3417
- Abstract
- In recent years there has been remarkable progress in one computer vision application area: object detection. One of the most challenging and fundamental problems in object detection is locating a specific object from the multiple objects present in a scene. Earlier traditional detection methods were used for detecting the objects with the introduction of convolutional neural networks. From 2012 onward, deep learning-based techniques were used for feature extraction, and that led to remarkable breakthroughs in this area. This paper shows a detailed survey on recent advancements and achievements in object detection using various deep learning techniques. Several topics have been included, such as Viola-Jones (VJ), histogram of oriented gradient (HOG), one-shot and two-shot detectors, benchmark datasets, evaluation metrics, speed-up techniques, and current state-of-art object detectors. Detailed discussions on some important applications in object detection areas, including pedestrian detection, crowd detection, and real-time object detection on Gpu-based embedded systems have been presented. At last, we conclude by identifying promising future directions. © 2020 by the authors.
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