Yong, S.-P. and Yeong, Y.-C. (2018) Human Object Detection in Forest with Deep Learning based on Drone's Vision. In: UNSPECIFIED.
Full text not available from this repository.Abstract
In the past decade, various new and impressive applications have been developed and implemented on drones, for instance search and rescue, surveillance, traffic monitoring, weather monitoring and so on. The current advances in drone technology provoked significant changes in enabling drones to perform a wide range of missions with increasing level of complexity. Missions such as search and rescue or forest surveillance require a large camera coverage and thus making drone a suitable tool to perform advanced tasks. Meanwhile, the increasing trend of deep learning applications in computer vision provides a remarkable insight into the initiative of this project. This paper presents a technique which allows detecting the existence of human in forestry environment with human object detection algorithm using deep learning framework. The purpose of detecting human existence in forestry area is to reduce illegal forestry activities such as illegal entry into prohibited area and illegal logging activities. Also, the outcome of this project is expected to aggrandize the usage of drone for forest surveillance purpose to save time and cost. © 2018 IEEE.
Item Type: | Conference or Workshop Item (UNSPECIFIED) |
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Additional Information: | cited By 26; Conference of 4th International Conference on Computer and Information Sciences, ICCOINS 2018 ; Conference Date: 13 August 2018 Through 14 August 2018; Conference Code:141665 |
Uncontrolled Keywords: | Aircraft detection; Crime; Drones; Logging (forestry); Monitoring; Object detection; Object recognition; Security systems; Timber, Forestry areas; Illegal logging; Learning frameworks; Object detection algorithms; Search and rescue; Traffic monitoring; Weather monitoring, Deep learning |
Depositing User: | Mr Ahmad Suhairi UTP |
Date Deposited: | 09 Nov 2023 16:36 |
Last Modified: | 09 Nov 2023 16:36 |
URI: | https://khub.utp.edu.my/scholars/id/eprint/9854 |