Research on an Improved YOLOv5 Algorithm for Biomass Renewable Energy Generation Technology

In order to solve the problems of low detection accuracy and poor robustness of biomass power generation systems caused by the diversity, occlusion, and complex industrial environment of renewable biomass fuel forms. This study proposes an enhanced YOLOv5 model for biomass power plant applications. This model integrates GSConv for lightweight feature extraction, bidirectional FPN-PAN network for multi-scale fuel characterization, and a novel SPPF/SPPELAN module for improving feature representation under noisy conditions. It is crucial to introduce a cross modal knowledge distillation framework to utilize semantic priors in large-scale models and enhance adaptability to heterogeneous biomass materials such as straw and sawdust. Experiments on enhanced datasets have shown that compared to traditional YOLOv5, precision has increased by 9.6%, recall has increased by 5.7%, F1 score has increased by 5.6%, while maintaining real-time performance, providing a feasible solution for the intelligent transformation of renewable energy facilities.