Assessment of Excavated Volume and Labor Investment at the Novotemirsky Copper Ore Mining Site

Chechushkov, Igor V.; Ankusheva, Polina S.; Ankushev, Maksim N.; Bazhenov, Evgeny A.; Alaeva, Irina P.

doi:10.1007/978-3-030-86040-0_18

Cited by 2 publications

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, artificial intelligence techniques have been utilized in the provisioning of civilian and military services [2]. In this context, drones have been adopted for express shipping and delivery [3][4][5], natural disaster prevention [6,7], geographical mapping [8], search and rescue operations [9], aerial photography for journalism and film making [10], providing essential materials [11], border control surveillance [12], and building safety inspection [13]. Even though drone technology offers a multitude of benefits, it raises mixed concerns when it comes to how it will be used in the future.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Tracking and Recognizing Drone Targets with Millimeter-Wave Radar and Convolutional Neural Network

Solaiman,

Alsuwat,

Alharthi

2023

ASI

View full text Add to dashboard Cite

In this paper, a framework for simultaneous tracking and recognizing drone targets using a low-cost and small-sized millimeter-wave radar is presented. The radar collects the reflected signals of multiple targets in the field of view, including drone and non-drone targets. The analysis of the received signals allows multiple targets to be distinguished because of their different reflection patterns. The proposed framework consists of four processes: signal processing, cloud point clustering, target tracking, and target recognition. Signal processing translates the raw collected signals into spare cloud points. These points are merged into several clusters, each representing a single target in three-dimensional space. Target tracking estimates the new location of each detected target. A novel convolutional neural network model was designed to extract and recognize the features of drone and non-drone targets. For the performance evaluation, a dataset collected with an IWR6843ISK mmWave sensor by Texas Instruments was used for training and testing the convolutional neural network. The proposed recognition model achieved accuracies of 98.4% and 98.1% for one and two targets, respectively.

show abstract