Herein， to realize high-precision crack and break defect detection in solar cells under electroluminescent （EL） conditions， the multi-scale You Only Look Once version 5（YOLOv5） model is used for solar-cell defect detection under real industrial conditions. First， an improved feature-extraction network combining deformable convolution version 2 （DCNv2） and coordinate attention （CA） is proposed to widen the receptive field of small target defects and enhance the extraction of small-scale defect features. Second， an improved path aggregation network （PANet）， called CA-PANet， is proposed for integrating the CA and cross-layer cascade in a path aggregation network to multiplex shallow features. Notably， the CA-PANet combines deep and shallow features to enhance the feature fusion of defects at different scales， improve the feature representation of defects， and increase the defect detection accuracy. The low computational cost of the lightweight CA ensures the real-time performance of the model. Experimental results indicate that the mean average precision（mAP） of the YOLOv5 model combining DCNv2 and CA can reach 95.4%， which is 3% higher than that of the YOLOv5 model and 1.4% higher than that of the YOLOX model. The improved YOLOv5 model can achieve a frame rate of up to 51 frames per second（FPS）， meeting industrial real-time requirements. Compared with other algorithms， the improved YOLOv5 model can accurately detect cracks and break defects in EL solar cells， satisfying the demand for real-time， high-precision defect detection under industrial conditions in photovoltaic power plants.