CFP last date
20 June 2024
Reseach Article

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

by Akpah Sylvester, Alese Boniface Kayode, Yao Yevenyo Ziggah
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%.

References
  1. E. Stemn, B. Kumi-Boateng, “Spatial Analysis of Artisanal and Small-Scale Mining in the Tarkwa-Nsuaem Municipality of Ghana”, Ghana Mining Journal, 20(1): 66–74, 2020.
  2. J. C. Hodgson, R. Mott, S. M. Baylis, T. T. Pham, S. Wotherspoon, A. D. Kilpatrick, R. R. Segaran, I. Reid, A. Terauds, L. P. Koh, N. Yoccoz, “Drones Count Wildlife More Accurately and Precisely than Humans”, Methods in Ecology and Evolution, 9(5): 1160–1167, 2018.
  3. Bhaskaranand, M. and Gibson, J. D. Low-Complexity Video Encoding for UAV Reconnaissance and Surveillance, In Proceedings of the IEEE Military Communications Conference (MILCOM), pages 1633–1638, IEEE, 2011.
  4. H. Püschel, M. Sauerbier, H.Eisenbeiss, A 3D Model of Castle Landenberg (CH) from Combined Photogrammetric Processing of Terrestrial and UAV-based Images.The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37: 93-98, 2008.
  5. A.Y.-M. Lin, A. Novo, S. Har-Noy, N. D. Ricklin, K. Stamatiou, “Combining GeoEye-1 Satellite Remote Sensing, UAV Aerial Imaging, and Geophysical Surveys in Anomaly Detection Applied to Archaeology”, Journal of Remote Sensing, 4: 870-876, 2011.
  6. R. Chandra,“FarmBeats: Automating Data Aggregation”, Farm Policy Journal, 15: 7-16, 2018.
  7. Bae, S. M., Han, K. H.., Cha, C. N. and Lee, H. Y. Development of Inventory Checking System Based on UAV and RFID in Open Storage Yard.In Proceedings of the International Conference on Information Science and Security (ICISS), pages 1-2, 2016.
  8. I. Sa, S. Hrabar, “Outdoor Flight Testing of a Pole Inspection UAV Incorporating Highspeed Vision”, Journal in Advanced Robotics, 105: 107-121, 2015.
  9. Moranduzzo, T. and Melgani, F. A SIFT-SVM method for detecting cars in UAV images.In Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, pages 6868-6871, IEEE, 2012.
  10. J. Linchant, J.Lisein, Semeki, J., P. Lejeune, C. Vermeulen, “Are Unmanned Aircraft Systems (UASS) the Future of Wildlife Monitoring? A Review of Accomplishments and Challenges”, Journal of Mammal Review,45: 239-252, 2015.
  11. R. D´ıaz-Delgado, M. Manez, A. Martınez, D. Canal, M. Ferrer, and D. Aragones, “Using UAVs to Map Aquatic Bird Colonies”The Roles of Remote Sensing in Nature Conservation, Springer,277-291, 2017.
  12. A. Hodgson, N. Kelly, D. Peel, “Unmanned Aerial Vehicles (UAVs) for Surveying Marine Fauna: A Dugong Case Study”, PloS One, 8(11): 1-15, 2013.
  13. I. Goodfellow, Y. Benjio, A. Courvile, “Deep Learning”,The MIT Press, pages 800, 2016.
  14. F. Chollet, “Deep Learning with Python”, Manning Publications, pages 384, 2017.
  15. Y. LeCun, Y. Bengio, G. Hinton, “Deep Learning”, Nature, 521: 436-444, 2015.
  16. Wu, J., Peng, B., Huang, Z. and Xie, J. Research on Computer Vision-Based Object Detection and Classification.In Proceedings of the International Conference on Computer and Computing Technologies in Agriculture, 2: pages 183-188, 2012.
  17. V. Wiley, T. Lucas, “Computer Vision and Image Processing: A Paper Review”, International Journal of Artificial Intelligence Research,2: 28-36, 2018.
  18. J. Schmidhuber, “Deep Learning in Neural Networks: An Overview”,Journal of Neural Networks, 61: 85-117, 2015.
  19. S. Ren, K. He, R. Girshick, J. Sun. “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39: 1137-1149, 2017.
  20. Krizhevsky, A., Sutskever, I. and Hinton, G. E. ImageNet Classification with Deep Convolutional Neural Networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems, 1: pages 1097-1105, 2012.
  21. He, K., Zhang, X., Ren, S. and Sun, J. Deep Residual Learning for Image Recognition. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778, 2016.
  22. Redmon, J., and Farhadi, A. YOLO9000: Better, Faster, Stronger. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, pages 6517-6525, 2017.
  23. Mantey, S. and Otoo, E. K. Comparison of Analytical and Numerical Water Influx Models in Bottom Water Reservoir. In Proceedings of 6thUMaT Biennial International Mining and Mineral Conference, pages 1-6, 2020.
  24. J. M. Kusimi, “Assessing Land Use and Land Cover Change in the Wassa West District of Ghana Using Remote Sensing”, Journal of Geology, 71: 249-259, 2008.
  25. P. L. Basommi, Q. Guan, D. Cheng, “Exploring Land Use and Land Cover Change in the Mining Areas of Wa East District, Ghana Using Satellite Imagery”, Journal of Open Geosciences, 7: 618-626, 2015.
  26. B. Snapir, D. M. Simms, T. W. Waine, “Mapping the Expansion of Galamsey Gold Mines in the Cocoa Growing Area of Ghana using Optical Remote Sensing”, International Journal of Applied Earth Observation and Geoinformation, 58: 225-233, 2017.
  27. F. Owusu-Nimo, J. Mantey, K. B.Nyarko, E. Appiah-Effah, A. Aubynn, "Spatial Distribution Patterns of Illegal Artisanal Small-Scale Gold Mining (Galamsey) operations in Ghana: A Focus on the Western Region", Journal of Heliyon,4(2): 1-36, 2018.
  28. G. Forkuor, T. Ullmann, M. Griesbeck, “Mapping and Monitoring Small-Scale Mining Activities in Ghana using Sentinel-1 Time Series (2015–2019)”, Journal of Remote Sensing, 12: 1-26, 2020.
  29. E. Ibrahim, L. Lema, P. Barnabé, P. Lacroix, E. Pirard,“Small-Scale Surface Mining of Gold Placers: Detection, Mapping, and Temporal Analysis through the use of Free Satellite Imagery”, International Journal of Applied Earth Observation and Geoinformation, 93: 1-11, 2020.
  30. P. de B. Pozzobon, D. de C. J. Osmar, F. G. Renato, A. T. G. Roberto, “Change Detection of Deforestation in the BrazilianAmazon using Landsat Data and Convolutional Neural Networks”, Journal of Remote Sensing,12(6): 1-9, 2020.
  31. R. Balaniuk, O. Isupova, S. Reece, “Mining and Tailings Dam Detection in Satellite Imagery Using Deep Learning”, Journal of Sensors, 20(23): 1-27, 2020.
  32. Q. Li, Z. Chen, B. Zhang, B. Li, K. Lu, L. Lu, H. Guo,“Detection of Tailings Dams Using High-ResolutionSatellite Imagery and a Single Shot MultiboxDetectorin the Jing–Jin–Ji Region, China”, Journal of Remote Sensing, 12(16): 1-18, 2020.
  33. C. Nyamekye, B. Ghansah, E. Agyapong, S. Kwofie, “Mapping Changes in Artisanal and Small-Scale Mining (ASM) Landscape using Machine and Deep Learning Algorithms - A Proxy Evaluation of the 2017 Ban on ASM in Ghana”, Elsevier,1-15, 2021.
  34. Zuiderveld, K. 1994) Contrast Limited Adaptive Histogram Equalization, In Graphics Gems; Heckbert, P.S., Ed.; Academic Press: Cambridge, MA, USA, pp. 474 – 485.
  35. Tzutalin. 2015. LabelImg (version 1.8.3). Gitcode.
  36. S.-H. Wang, V. V. Govindaraj, J. M. Górriz, X. Zhang, Y. D. Zhang, “Covid-19 classification by FGCNet with Deep Feature Fusion from Graph Convolutional Network and Convolutional Neural Network”, Journal of Information Fusion, 67: 208–229, 2021.
  37. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. 2016. You Only Look Once: Unified, Real-Time Object Detection.InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 779 – 788.
  38. Redmon, J., and Farhadi, A. 2017. YOLO9000: Better, Faster, Stronger, In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, pages 6517–6525.
  39. Redmon, J. and Farhadi, A. 2018. YOLOv3: An incremental improvement, https://arxiv.org/pdf/1804.02767.pdf, (January, 2021),1 – 6.
  40. Bochkovskiy, C.-Y., Wang, C.-Y. and Liao, H-Y. M. 2020. YOLOv4: Optimal Speed and Accuracy of Object Detection, https://arxiv.org/pdf/2004.10934.pdf, (January, 2021), 1-18.
  41. He, K., Zhang, X., Ren, S. and Sun, J. 2014.Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.In Proceedings of the 13th European Conference in Computer Vision, pages 6-12.
  42. Liu, S.; Qi, L.; Qin, H.; Shi, J.; Jia, J. 2018. Path Aggregation Network for Instance Segmentation. In Proceedings of the 2018 IEEE/CVFConference on Computer Vision and Pattern Recognition (CVPR), pages 8759 – 8768.
  43. Wang, C Y., Liao, H Y., Wu, Y H., Chen, P-Y., Hsieh, J-W. and Yeh, I-H. 2020.CSPNet:A New Backbone that can Enhance Learning Capability of CNN. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 390-391.
  44. O. Janssens, V.Slavkovikj, B. Vervisch, K. Stockman, M. Loccufier, S. Verstockt, R. Van De Walle, S. Van Hoecke, “Convolutional Neural Network Based Fault Detection for Rotating Machinery”, Journal of Sound and Vibration, 377: 331-345, 2016.
  45. M. A. Alsalem,A. A. Zaidan,M. Hashim,O. S. Albahri, A. S. Albahri, A. K. Hadi, I.Mohammed, “Systematic Review of an Automated Multiclass Detection and Classification System for Acute Leukaemia in Terms of Evaluation and Benchmarking, Open Challenges, Issues and Methodological Aspects”, Journal of Medical Systems,42: pages 204, 2018.
  46. Kingma, D. P. and Ba, J. 2014Adam: A Method for Stochastic Optimization. In Proceedings of theInternational Conference on Learning Representations, pages 1-15.
Index Terms

Computer Science
Information Sciences

Keywords

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