HOTSPOT-YOLO: A Lightweight Deep Learning Attention-Driven Model for Detecting Thermal Anomalies in Drone-Based Solar Photovoltaic Inspections

• URL: http://arxiv.org/abs/2508.18912v1
• Date: Tue, 26 Aug 2025 10:35:24 GMT
• Title: HOTSPOT-YOLO: A Lightweight Deep Learning Attention-Driven Model for Detecting Thermal Anomalies in Drone-Based Solar Photovoltaic Inspections
• Authors: Mahmoud Dhimish,
• Abstract summary: HotSPOT-YOLO is a lightweight artificial intelligence (AI) model that integrates an efficient convolutional neural network backbone and attention mechanisms to improve object detection.<n>This work highlights the integration of advanced AI techniques with practical engineering applications, revolutionizing automated fault detection in renewable energy systems.
• Score: 0.685316573653194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Thermal anomaly detection in solar photovoltaic (PV) systems is essential for ensuring operational efficiency and reducing maintenance costs. In this study, we developed and named HOTSPOT-YOLO, a lightweight artificial intelligence (AI) model that integrates an efficient convolutional neural network backbone and attention mechanisms to improve object detection. This model is specifically designed for drone-based thermal inspections of PV systems, addressing the unique challenges of detecting small and subtle thermal anomalies, such as hotspots and defective modules, while maintaining real-time performance. Experimental results demonstrate a mean average precision of 90.8%, reflecting a significant improvement over baseline object detection models. With a reduced computational load and robustness under diverse environmental conditions, HOTSPOT-YOLO offers a scalable and reliable solution for large-scale PV inspections. This work highlights the integration of advanced AI techniques with practical engineering applications, revolutionizing automated fault detection in renewable energy systems.

Related papers

• Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
  The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
  arXiv  Detail & Related papers  (2026-01-25T12:47:25Z)
• SGEMAS: A Self-Growing Ephemeral Multi-Agent System for Unsupervised Online Anomaly Detection via Entropic Homeostasis [0.0]
  We introduce SGEMAS (Self-Growing Ephemeral Multi-Agent System), a bio-inspired architecture that treats intelligence as a dynamic thermodynamic process.<n>In a challenging inter-patient, zero-shot setting, the final SGEMAS v3.3 model achieves a mean AUC of 0.570 +- 0.070, outperforming both its simpler variants and a standard autoencoder baseline.
  arXiv  Detail & Related papers  (2025-12-08T00:43:51Z)
• Physics-Informed Large Language Models for HVAC Anomaly Detection with Autonomous Rule Generation [2.5352713493505785]
  Heating, Ventilation, and Air-Conditioning systems account for a substantial share of global building energy use.<n>We present PILLM, a Physics-Informed LLM framework that operates within an evolutionary loop to automatically generate, evaluate, and refine anomaly detection rules.<n>Our approach introduces physics-informed reflection and crossover operators that embed thermodynamic and control-theoretic constraints, enabling rules that are both adaptive and physically grounded.
  arXiv  Detail & Related papers  (2025-10-20T04:43:36Z)
• Benchmarking Vision Transformers and CNNs for Thermal Photovoltaic Fault Detection with Explainable AI Validation [0.0]
  This study provides a systematic comparison of convolutional neural networks (ResNet-18, EfficientNet-B0) and vision transformers (ViT-Tiny, Swin-Tiny) for thermal PV fault detection.<n> Evaluation on 20,000 infrared images spanning normal operation and 11 fault categories shows that Swin Transformer achieves the highest performance.<n> XRAI analysis reveals that models learn physically meaningful features, such as localized hotspots for cell defects, linear thermal paths for diode failures, and thermal boundaries for vegetation shading.
  arXiv  Detail & Related papers  (2025-09-08T08:38:53Z)
• Lightweight LSTM Model for Energy Theft Detection via Input Data Reduction [0.0]
  This paper proposes a lightweight detection unit, or watchdog mechanism, designed to act as a pre-filter.<n>It reduces the volume of input fed to the LSTM model, limiting it to instances that are more likely to involve energy theft.<n>Results indicate a power consumption reduction exceeding 64%, with minimal loss in detection accuracy and consistently high recall.
  arXiv  Detail & Related papers  (2025-06-14T14:40:03Z)
• Enhanced PEC-YOLO for Detecting Improper Safety Gear Wearing Among Power Line Workers [0.0]
  This paper proposes an enhanced PEC-YOLO object detection algorithm.<n>The method integrates deep perception with multi-scale feature fusion.<n>The CPCA attention mechanism is incorporated into the SPPF module, improving the model's ability to focus on critical information.
  arXiv  Detail & Related papers  (2025-01-23T04:40:08Z)
• Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning [52.64813150003228]
  The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring.<n>In the upcoming six-generation (6G) era, UAV-assisted mobile edge computing (MEC) is particularly crucial in challenging environments such as mountainous or disaster-stricken areas.<n>The task offloading problem is one of the key issues in UAV-assisted MEC, primarily addressing the trade-off between minimizing the task delay and the energy consumption of the UAV.
  arXiv  Detail & Related papers  (2025-01-11T02:32:42Z)
• AI-Powered Dynamic Fault Detection and Performance Assessment in Photovoltaic Systems [44.99833362998488]
  intermittent nature of photovoltaic (PV) solar energy leads to power losses of 10-70% and an average energy production decrease of 25%. Current fault detection strategies are costly and often yield unreliable results due to complex data signal profiles. This research presents a computational model using the PVlib library in Python, incorporating a dynamic loss quantification algorithm.
  arXiv  Detail & Related papers  (2024-08-19T23:52:06Z)
• Grid Monitoring with Synchro-Waveform and AI Foundation Model Technologies [41.994460245857404]
  This article advocates for the development of a next-generation grid monitoring and control system designed for future grids dominated by inverter-based resources.<n>We develop a physics-based AI foundation model with high-resolution synchro-waveform measurement technology to enhance grid resilience and reduce economic losses from outages.
  arXiv  Detail & Related papers  (2024-03-11T17:28:46Z)
• A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation [9.784061533539822]
  convolutional neural network (CNN) has been widely used in the existing automatic defect detection of PV cells. We propose a novel lightweight high-performance model for automatic defect detection of PV cells based on neural architecture search and knowledge distillation. The proposed lightweight high-performance model can be easily deployed to the end devices of the actual industrial projects and retain the accuracy.
  arXiv  Detail & Related papers  (2023-02-15T04:00:35Z)
• CellDefectNet: A Machine-designed Attention Condenser Network for Electroluminescence-based Photovoltaic Cell Defect Inspection [67.99623869339919]
  A big challenge faced by industry in photovoltaic cell visual inspection is the fact that it is currently done manually by human inspectors. In this work, we introduce CellDefectNet, a highly efficient attention condenser network designed via machine-driven design exploration. We demonstrate the efficacy of CellDefectNet on a benchmark dataset comprising of a diversity of photovoltaic cells captured using electroluminescence imagery.
  arXiv  Detail & Related papers  (2022-04-25T16:35:19Z)
• A Multi-Stage model based on YOLOv3 for defect detection in PV panels based on IR and Visible Imaging by Unmanned Aerial Vehicle [65.99880594435643]
  We propose a novel model to detect panel defects on aerial images captured by unmanned aerial vehicle. The model combines detections of panels and defects to refine its accuracy. The proposed model has been validated on two big PV plants in the south of Italy.
  arXiv  Detail & Related papers  (2021-11-23T08:04:32Z)
• Meta-UDA: Unsupervised Domain Adaptive Thermal Object Detection using Meta-Learning [64.92447072894055]
  Infrared (IR) cameras are robust under adverse illumination and lighting conditions. We propose an algorithm meta-learning framework to improve existing UDA methods. We produce a state-of-the-art thermal detector for the KAIST and DSIAC datasets.
  arXiv  Detail & Related papers  (2021-10-07T02:28:18Z)
• Evaluation of electrical efficiency of photovoltaic thermal solar collector [1.9604116035607555]
  This study uses machine learning methods to predict the thermal performance of a photovoltaic-thermal solar collector (PV/T) The inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the inputs variables. The proposed LSSVM model outperformed ANFIS and ANNs models.
  arXiv  Detail & Related papers  (2020-02-11T21:11:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.