2026-10-15T09:25:38Z https://api.figshare.com/v2/oai

oai:figshare.com:article/30030226 2025-09-04T13:18:19Z category_26503 category_28879 portal_918 item_type_3 month_year_09_2025

Evaluation of a Low Power Computer Vision Based Positioning System for a Handheld Landmine Detector using AprilTag Markers - Supporting Data Adam Fletcher (22166539) John Davidson (22166582) Edward Cheadle (22166585) Daniel Conniffe (22166689) Anthony Peyton (22166662) Frank Podd (11223981) Electronic sensors Computer vision machine vision positioning fiducial markers landmine <p dir="ltr">A positioning system employing visual fiducial markers (AprilTags) has been implemented for use with handheld mine detection equipment. To be suitable for a battery powered real-time application, the system has been designed to operate at low-power (<100 mW), and frame-rates between 30 and 50 fps. The system has been integrated into an experimental dual mode detector system. Position indexed metal detector and ground penetrating radar data from laboratory and field trials are presented. The accuracy and precision of the vision based system are found to be 1.2 cm and 0.5 cm respectively.</p> 2025-09-04T13:18:19Z Dataset Dataset 10.48420/30030226.v1 https://figshare.com/articles/dataset/Evaluation_of_a_Low_Power_Computer_Vision_Based_Positioning_System_for_a_Handheld_Landmine_Detector_using_AprilTag_Markers_-_Supporting_Data/30030226 CC BY-NC 4.0