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

TL;DR

HOTSPOT-YOLO is a lightweight, attention-driven deep learning model designed for real-time thermal anomaly detection in drone inspections of solar PV systems, significantly improving accuracy and efficiency.

Contribution

This paper introduces HOTSPOT-YOLO, a novel lightweight AI model that combines an efficient CNN backbone and attention mechanisms for enhanced thermal anomaly detection in PV inspections.

Findings

Achieved 90.8% mean average precision in thermal anomaly detection.

Reduced computational load enables real-time drone-based inspections.

Demonstrated robustness across diverse environmental conditions.

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.