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Object Detection in Artisanal Small-Scale Gold Mining Environments using a Modified YOLOv4 Algorithm

by Akpah Sylvester, Alese Boniface Kayode, Yao Yevenyo Ziggah
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 50
Year of Publication: 2022
Authors: Akpah Sylvester, Alese Boniface Kayode, Yao Yevenyo Ziggah
10.5120/ijca2022921906

Akpah Sylvester, Alese Boniface Kayode, Yao Yevenyo Ziggah . Object Detection in Artisanal Small-Scale Gold Mining Environments using a Modified YOLOv4 Algorithm. International Journal of Computer Applications. 183, 50 ( Feb 2022), 36-49. DOI=10.5120/ijca2022921906

@article{ 10.5120/ijca2022921906,
author = { Akpah Sylvester, Alese Boniface Kayode, Yao Yevenyo Ziggah },
title = { Object Detection in Artisanal Small-Scale Gold Mining Environments using a Modified YOLOv4 Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2022 },
volume = { 183 },
number = { 50 },
month = { Feb },
year = { 2022 },
issn = { 0975-8887 },
pages = { 36-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number50/32267-2022921906/ },
doi = { 10.5120/ijca2022921906 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:15:25.527506+05:30
%A Akpah Sylvester
%A Alese Boniface Kayode
%A Yao Yevenyo Ziggah
%T Object Detection in Artisanal Small-Scale Gold Mining Environments using a Modified YOLOv4 Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 50
%P 36-49
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Detecting objects in high-resolution images could be a very challenging task, particularly, when analysing remote sensing imagery captured with an Unmanned Aerial Vehicle (UAV) at Artisanal Small-Scale Gold Mining (ASGM) environments. Due to the heterogeneous nature of ASGM environments in Ghana, object detection algorithms are prone to misclassification errors in identifying irrelevant ground objects for target objects. In recent times, research into Convolutional Neural Networks (CNNs) for object detection has gained immense popularity which can be attributed to its proven dominance to efficiently learn and extract image features. This study proposes a modified You Only Look Once (YOLO) algorithm known as ASGM-YOLO which is based on the YOLOv4 framework to detect objects of interest such as excavators, sluice boards, tailings dump, crushers, persons, and trucks from UAV captured images at ASGM sites. The goal is to monitor illegal ASGM activities by detecting these objects quickly so that further damage to the environment can be stopped. The ASGM-YOLO algorithm is a single-stage object detector that adopts an end-to-end detection approach to predict class probabilities and bounding boxes around objects faster with optimal accuracy. The detection accuracy of the proposed ASGM-YOLO algorithm was compared to other algorithms and the results showed that the ASGM-YOLO performed better by achieving a detection accuracy of 96.50%.

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Index Terms

Computer Science
Information Sciences

Keywords

Artisanal Small-Scale Gold Mining YOLOv4 Algorithm Unmanned Aerial Vehicle Object Detection Galamsey

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