Satellite Image Segmentation using Modified U-Net Convolutional Networks

Subraja, N.; Venkatasekhar, D.

doi:10.1109/icscds53736.2022.9760787

Cited by 3 publications

(1 citation statement)

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“…In this article, we explore the use of deep learning and pre-trained models, specifically, ResNet50 [ 13 ] and MobileNet [ 14 ], for the detection of solar modules in IRT images. These pre-trained models are convolutional neural networks [ 15 ] that have been trained on massive and diverse datasets, enabling them to learn general and complex features that can be applied to specific tasks, such as object detection.…”

Section: Introductionmentioning

confidence: 99%

IoT System Based on Artificial Intelligence for Hot Spot Detection in Photovoltaic Modules for a Wide Range of Irradiances

Cardinale-Villalobos,

Jimenez-Delgado,

García-Ramírez

et al. 2023

Sensors

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Infrared thermography (IRT) is a technique used to diagnose Photovoltaic (PV) installations to detect sub-optimal conditions. The increase of PV installations in smart cities has generated the search for technology that improves the use of IRT, which requires irradiance conditions to be greater than 700 W/m2, making it impossible to use at times when irradiance goes under that value. This project presents an IoT platform working on artificial intelligence (AI) which automatically detects hot spots in PV modules by analyzing the temperature differentials between modules exposed to irradiances greater than 300 W/m2. For this purpose, two AI (Deep learning and machine learning) were trained and tested in a real PV installation where hot spots were induced. The system was able to detect hot spots with a sensitivity of 0.995 and an accuracy of 0.923 under dirty, short-circuited, and partially shaded conditions. This project differs from others because it proposes an alternative to facilitate the implementation of diagnostics with IRT and evaluates the real temperatures of PV modules, which represents a potential economic saving for PV installation managers and inspectors.

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Section: Introductionmentioning

confidence: 99%