Photovoltaic panel thermal anomaly detection using drone imagery and optimized YOLOv8 thermal image object detection model for solar farm maintenance | |
| Author | Sarit Tristan Pietersz |
| Call Number | AIT Project no.PMDS-23-05 |
| Subject(s) | Machine learning Solar energy--Data processing |
| Note | A project study submitted in partial fulfillment of the requirements for the degree of Professional Master in Data Science and Artificial Intelligence Applications |
| Publisher | Asian Institute of Technology |
| Abstract | This study investigates the effectiveness of the YOLOv8 object detection model in enhancing hotspot anomaly detection within solar farms, with a primary focus on improving maintenance processes' efficiency and accuracy. The examination encompasses a comprehensive analysis of challenges associated with hotspot detection, delves into the architectural intricacies of the YOLOv8 model, and outlines tailored training procedures. The resulting optimized model, YOLOv8-OPT, demonstrates significant improvements, boasting a 3.3% increase in precision, a substantial 12.7% improvement in recall, and commendable progress in mean average precision (mAP50 / mAP50-95) by 6.8% and 8%, respectively. However, in comparison to a preceding research model operating on a more constrained dataset, YOLOv8-OPT reveals certain limitations, reflecting a 61.3% decrement in precision and a 53.2% reduction in recall. In this comparative landscape, YOLOv8- OPT emerges as a preeminent model, showcasing superiority across various metrics. This includes a 5.08% increase in precision, a 6.90% increase in recall, a remarkable 16.67% increase in mAP50, a notable 38.89% enhancement in mAP50-95, and a 5.08% increase in F1 Score. The accompanying ablation study underscores the pivotal role of hyperparameters, emphasizing the need for meticulous tuning to achieve optimal model efficacy. While YOLOv8-OPT signifies advancements, it underscores the importance of carefully considering dataset nuances and the imperative for ongoing optimization in propelling future developments in object detection methodologies. |
| Year | 2023 |
| Type | Project |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Professional Master in Data Science and Artificial Intelligence Applications (PMDS) |
| Chairperson(s) | Cherdsak Kingkan (Co-Chairperson);Chutiporn Anutariya (Co-Chairperson); |
| Examination Committee(s) | Vatcharaporn Esichaikul;Chantri Polprasert; |
| Degree | Professional Master in Data Science and Artificial Intelligence Applications - Asian Institute of Technology, 2023 |