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Anton Yudhana
Department of Electrical Engineering, Faculty of Industrial Technology, Universitas Ahmad Dahlan
Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Vision-guided tracking and detection using the YOLOv5 model on a logistic delivery fixed-wing UAV Son Ali Akbar; Muhammad Taufiq Dinar Akbar; Anton Yudhana; Kamarul Hawari Ghazali
SINERGI Vol. 30 No. 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.2.011

Abstract

Deep learning technologies utilizing Convolutional Neural Networks (CNNs) have advanced the development of autonomous systems, particularly in the exploration of hazardous environments. This study integrates the YOLOv5 object detection model with a fixed-wing Unmanned Aerial Vehicle (UAV) to identify simulated 5x5-meter orange marker dropping kits deployed in inaccessible disaster zones. Experiments were conducted at altitudes of 45 m, 75 m, and 100 m above sea level to assess real-time detection accuracy and terrain-mapping efficiency. The system achieved a mean Average Precision (mAP) of 88% across varying altitudes, demonstrating robust performance despite environmental challenges such as false positives from similarly colored rooftops. Computational efficiency tests were performed on the Jetson Nano platform using the TensorRT engine to accelerate object detection model inference on NVIDIA GPUs. Lighting variability significantly impacted detection reliability, resulting in a reduced mAP under suboptimal illumination. To enhance precision, post-processing filters and parameter optimizations were applied, improving the balance between detection sensitivity and specificity. These findings underscore the potential of YOLOv5-enabled UAVs for rapid, high-accuracy aid localization in disaster scenarios, although adaptive threshold tuning remains critical to address environmental variability in operational settings.
Co-Authors Kamarul Hawari Ghazali Muhammad Taufiq Dinar Akbar Son Ali Akbar