A Novel Deep Learning Approach for Heliostat Dirt Segmentation and Severity Assessment

Photothermal power generation is a promising technique for converting solar radiation into electricity with high efficiency and stability. However, the performance and maintenance of photothermal power plants depend on the cleanliness and reflectivity of the heliostats. This paper introduces an innovative approach to addressing the challenges of dirt detection and segmentation on heliostats. Leveraging the capabilities of deep learning, we propose the Multi-Scale Heliostat Dirt Segmentation and Classification (MSHDSC) framework, integrating a novel multi-scale feature fusion module (MSFFM) with an enhanced DeepLabV3+ network. This framework effectively segments small dirt areas on heliostat images, facilitating precise cleaning strategies. A unique aspect of our work is the introduction of an unsupervised clustering algorithm post-segmentation, which categorizes dirt based on color and texture, assigning a severity score to each category. This categorization assists in determining the cleaning complexity and prioritizing maintenance efforts. Experimental results show that our method outperforms several state-of-the-art image segmentation models in terms of accuracy and efficiency and provides useful information for targeted and prioritized cleaning of heliostats by robots or drones.