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

2025-08-26

AI Summary

Detecting subtle thermal anomalies (e.g., hotspots, defective modules) in photovoltaic (PV) systems under drone-captured thermal imaging remains challenging due to low contrast, small object size, and poor real-time performance. To address this, we propose a lightweight end-to-end object detection framework. Our method integrates an efficient convolutional backbone with a channel-spatial collaborative attention mechanism to enhance feature representation, and incorporates multi-scale feature fusion guided by thermal imaging priors to improve sensitivity to low-contrast, sub-pixel anomalies. Evaluated on a real-world drone-based thermal inspection dataset, our model achieves 90.8% mAP—outperforming state-of-the-art lightweight detectors (YOLOv5s, NanoDet) by an average of 6.2%. It runs at 32 FPS on a Jetson AGX Orin, striking a strong balance among accuracy, inference speed, and environmental robustness. This work delivers a deployable solution for large-scale intelligent PV operation and maintenance.

Technology Category

• Computer Vision: Object Detection & Categorization
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Detecting thermal anomalies in drone-based solar PV inspections
• Addressing challenges of small subtle thermal anomaly detection
• Maintaining real-time performance while reducing computational load

Innovation

• Lightweight AI model with attention mechanisms
• Efficient convolutional neural network backbone
• Real-time drone-based thermal anomaly detection