Deep learning technique for Swamp deer detection Using Cost-Effective UAVs
Description
This study enhances drone capabilities for wildlife detection, focusing on swamp deer (Rucervus duvaucelii). We used YOLO V3, V5, V7, V8, Object Detection V3, and DETR models. We prepared a non-GPU Real-time detection using frame sampling technique, making it cost-effective and accessible, suitable for conservation efforts and adaptable to other species monitoring. Total images - 8210 UAV Aerial image - 6765 Handheld camera - 1445 UAV utilized - DJI Mavic 2 Zoom, DJI Mavic 2 Enterprise, and DJI Mavic Pro YOLO V3 Train – 6198, Test – 2012 Others (YOLO V5, V7, V8, DETR, Object detection) Train-6198, Test- 687, Validate- 1325 Real-time - Using frame skipping technique, The YOLO V5 model has shown outstanding performance when applied to video with 19 skipped frames at a resolution of 320 pixels and 32 frames per second (fps). The Swamp deer dataset was annotated manually using Labelmg offline tools (https://pypi.org/project/labelImg/) and Roboflow online platform (https://app.roboflow.com/)
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- Planning and management for aquatic species conservation and 580 maintenance of ecosystem services in the Ganga River basin for a clean GangaGrant ID: (Grant No. B-03/2015- 581 16/1077/NMCG-New proposal)