SNCF-Net: Scale-aware neighborhood correlation feature network for hotspot defect detection of photovoltaic farms

Abstract

The photovoltaic hotspot defect detection is challenging due to the features vanishing as the network deepens and the poor feature discrimination ability under complex background interference. To address this challenging problem, we first designed a novel neighborhood correlation feature module (NCFM) to adaptively Integrate information from different scales based on the correlation between features to alleviate the problem of small defects feature loss. Then, we constructed a Scale-aware Attention Mechanism (SAM), which adaptively reweights the channel features and adjusts the supervision signals at different scales to enhance the utilization of features. Finally, we designed a Scale-aware Neighborhood Correlation Feature Network (SNCF-Net), which performs well in photovoltaic inspection hotspot defect localization. The experimental results demonstrated that SNCF-Net achieves 95.2 % (F-measure), 89.7 % (mAP), and 54.3 % (IoU) in terms of hotspot defects classification and detection results in infrared images of photovoltaic farms, which outperforms the current state-of-the-art methods.