Jothibasu, M and Sowmiya, M and Harsha, R and Naveen, K S and Suriyaprakash, T B (2023) Improvement of computer vision-based elephant intrusion detection system (EIDS) with deep learning models. In: Innovative Engineering with AI Applications. Wiley Blackwell, pp. 131-153. ISBN 9781119792161
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Abstract
The expanding need of wild life behavior and partition of human-wild life strife has guided the researchers to the execution of counteraction and alleviation draws near. The division of boondocks grounds and separation of elephant populaces moves toward the essential driver of Human Elephant Collision (HEC). Humanelephant strife is a troublesome issue since which prompts to territory misfortune, destruction of agribusiness zones, environment misfortune. This demands an intelligent system which predicts and rest the conflict. In this work, Artificial Intelligent based device is developed to detect the presence of elephant into residential areas and provides a warning to the forest rangers. In this counterfeit intelligent gadget, the identification part comprises of the PIR sensor and seismic sensor. Both of the sensor yields trigger the camera module associated with the model created. The camera module catches a video for a specific span which may contain the nearness of the animal. At that point, the recordings are been coordinated to the trained AI algorithm. The AI algorithm forms the video in the succession of the casing and once if the presence of an elephant is available in the video, it triggers the cautioning system. An SSD, YOLO, and Faster RCNN algorithm has been accomplished to locate the ideal one. The model builds up a broad item to distinguish an economical arrangement.
Item Type: | Book Section |
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Uncontrolled Keywords: | Deep learning; Faster RCNN; Human elephant conflict; PIR sensor; YOLO |
Subjects: | A Artificial Intelligence and Data Science > Deep Learning A Artificial Intelligence and Data Science > Animation A Artificial Intelligence and Data Science > Artificial intelligence |
Divisions: | Electronics and Communication Engineering |
Depositing User: | Users 5 not found. |
Date Deposited: | 13 Jul 2024 03:53 |
Last Modified: | 13 Jul 2024 03:53 |
URI: | https://ir.psgitech.ac.in/id/eprint/752 |