Biomimetic model of photovoltaic cell defect detection based on mimic vision

Solar energy plays an important role in new power systems, and defect detection on photovoltaic (PV) cells is becoming more and more important for the transition of power systems to clean energy. Traditional target detection models are difficult to identify tiny defects on PV cells with complex textures, for this reason, we propose anthropomorphic vision biomimetic detection models. Firstly, a backbone network inspired by the human sensory field and peripheral vision mechanisms is proposed to design the biomimetic visual attention mechanism as well as the biomimetic feature extraction module to fully extract the dynamic context and perceive the peripheral visual attention, and correlate the two in order to capture the fine-grained features of the defective target in the noise-filled background. Second, in the feature fusion stage, a separated spatial semantic fusion pyramid is designed based on the human brain information transfer mode, and semantic and spatial information transfer modules are designed in different information transfer paths to enhance the ability of defective features to express spatial and semantic information. Then, inspired by the partitioning mechanism of the brain cortex, we propose a cascade detection head for task alignment, adaptive regulatory modulation of features at different scales, alignment of spatial mismatches in classification and localization tasks, and separated design of the structure of classification and localisation branches. Finally, the effectiveness of the model is demonstrated experimentally.