Implementing Deep Learning for Real-Time Fuel Tank Detection in UAV Surveillance Systems

Abstract

This study investigates the performance of the YOLOv8 object detection algorithm for fuel tank detection using unmanned aerial vehicles (UAVs) under various conditions and perspectives. UAVs, with their agile mobility and ability to capture high-quality images from different angles, are proven to facilitate the detection of fuel tanks, which play a critical role in various industries. The integration of UAVs with YOLOv8 has led to significant advancements in automation and precision in inspection processes within the energy and industrial sectors. YOLOv8, a single-stage deep learning-based object detection model, demonstrates superior architecture compared to previous YOLO iterations, showcasing its strengths in speed and accuracy for object detection tasks. The model achieves a precision of 0.888, recall of 0.896, and mAP of 0.891, confirming its strong capabilities in detecting fuel tanks and supporting the sustainability of industrial and energy operations. With a processing time of 41 ms, YOLOv8 proves to be highly effective for real-time applications. This research highlights the importance of optimizing UAVs and deep learning models for reliable data collection in challenging environments and demonstrates their potential for use in fuel tank monitoring and infrastructure reliability tracking across industries. While accurate detection successes are noted, the study emphasizes the need for further optimization of the algorithm to address false positives and undetected objects in real-world applications. Future work will explore the adaptation of high-performance algorithms such as YOLOv8 for broader object detection scenarios and their testing under diverse environmental conditions.