You Only Look Once on Power Line Components: An Object Detection Using Unmanned Aerial Vehicle

Sumagayan, Moheddin U.; Aleluya, Earl Ryan M.; Cahig, Christian; Librado, Lester G.; Mangorsi, Rohanni B.; Arda, Margie S.; Estoperez, Noel R.; Salaan, Carl John

doi:10.1109/hnicem54116.2021.9731867

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…In contrast to other detection models, such as Single Shot Multi-box Detector (SSD) and RetinaNet, YOLO provides frequent updates, resulting in faster processing and higher accuracy [25]. It has been a popular architecture as it redefines the object detection task as a regression problem, which significantly increases the processing speed [26].…”

Section: B Stage 1: Oil Tank Detectionmentioning

confidence: 99%

Automatic Monitoring of Oil Tank 3D Geometry and Storage Changes With Interferometric Coherence and SAR Intensity Information

Tsai,

Huang,

Chen

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Continuous monitoring of oil tanks is vital for analyzing local fuel consumption. Synthetic Aperture Radar (SAR) has been a popular data source as it guarantees day-and-night and all-weather sensing capacity. However, most earlier studies adopt a scene-wise and oil tank-wise scheme, which is inefficient as there can be hundreds of oil tanks on an oil depot, while only a few are dynamic. Also, no study explores both intensity coherence and interferometric coherence for oil tank dynamics mapping. This study proposes a novel three-stage strategy to detect all oil tanks, identify dynamic oil tanks, and estimate their fuel volume changes based on both the intensity and phase information of SAR in both slant-range and geocoded projections. Results indicate that the intensity coherence can perfectly differentiate dynamic and stable oil tanks (a Jeffries-Matusita distance of 1.997) and is less vulnerable to repeat-pass SAR factors, such as baselines and atmospheric conditions. Via evaluating estimations' consistency, our scattering keypoint detection exhibits 0.23 and 0.87 m precision of tank heights and diameters, respectively. By validation with ground truth data, oil tanks exhibiting floatingroof changes larger than 0.23 m are correctly identified. Also, the estimated storage changes agree well with actual changes with an R-squared value of 0.98 and a root-mean-square error (RMSE) corresponding to 1.05 m biases in floating-roof heights. These quantitative assessments confirm the robustness and broad applicability of our non-in situ data-needed approach, highlighting the opportunity to utilize spotlight SAR data to automatically and comprehensively monitor oil tank dynamics in remote sites.

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Section: B Stage 1: Oil Tank Detectionmentioning

confidence: 99%