EFEM-YOLO: An efficient feature extraction network for surface defect detection of photovoltaic cell

Abstract Stable operation of photovoltaic (PV) cells is crucial for reliable and efficient electricity generation in power systems. However, their poor defect characterization, large size variation, and high background noise lead to low automatic identification accuracy. In this paper, a YOLO-based network with an efficient feature extraction network, termed EFEM-YOLO, is proposed for accurate defect detection in PV cells. First, a multi-scale dilated convolution feature pyramid (MDCFP) module is proposed. By constructing an adaptive feature pyramid via parallel convolutional paths, it enhances the representation of multi-scale defects. Second, the C2f-dynamic gated activation network (C2f-DGAN) is introduced. By incorporating a dynamic gated nonlinear activation mechanism and a cross-stage dual-branch feature aggregation strategy, the model's adaptability to multi-scale defects and recognition accuracy are improved. Finally, a novel SmartShapeIoU loss function is proposed. This mitigates localization bias caused by object scale variations in high-noise environments, thereby improving bounding box regression accuracy. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods in detection accuracy. Additional dataset experiments validate the superior detection accuracy and generalization of the proposed method for small object detection.