A
Akashdeep Sharma
Researcher at Panjab University, Chandigarh
Publications - 34
Citations - 321
Akashdeep Sharma is an academic researcher from Panjab University, Chandigarh. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 4, co-authored 16 publications receiving 81 citations.
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Deep learning-based object detection in low-altitude UAV datasets: A survey
TL;DR: A comprehensive review of the state of the art deep learning based object detection algorithms and analyze recent contributions of these algorithms to low altitude UAV datasets is provided.
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Soft Computing based object detection and tracking approaches: State-of-the-Art survey
TL;DR: The study is novel as it traces rise of soft computing methods in field of object detection and tracking in videos which has been neglected over the years and provides number of analyses to guide future directions of research.
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Scaling up face masks detection with YOLO on a novel dataset
TL;DR: A novel face masks detection dataset consisting of 52,635 images with more than 50,000 tight bounding boxes and annotations for four different class labels namely, with masks, without masks, masks incorrectly, and mask area is proposed which makes it a novel contribution for variety of face masks classification and detection tasks.
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A hybrid tiny YOLO v4-SPP module based improved face mask detection vision system
TL;DR: In this paper, the authors proposed a novel face mask vision system that is based on an improved tiny YOLO v4 object detector, which achieved a mAP (mean average precision) value of 64.31% on the employed dataset.
Journal ArticleDOI
IoT and deep learning-inspired multi-model framework for monitoring Active Fire Locations in Agricultural Activities
Akashdeep Sharma,Harish Kumar,Kapish Mittal,Sakshi Kauhsal,Manisha Kaushal,Divyam Gupta,Abheer Narula +6 more
TL;DR: The proposed framework provides a software module for monitoring and tracking of various AFL and comes with several features like automatic extraction of fire locations from remote sensing sites, assigning active fire locations to multiple stakeholders, extracting farmers' names indulged in burning, and provisions for citizens centric participation.