This paper obtains image datasets through electroluminescence imaging and uses deep learning image detection algorithms to identify defects in solar cells. We improve the YOLOv4 target detection algorithm by replacing the backbone network of the original algorithm with DenseNet121 and connecting the feature image information through the dense blocks in DenseNet121 to increase the detection accuracy and speed. We also enhance the nonmaximum suppression (NMS) of the original algorithm with Softer-NMS to improve the positioning accuracy of the bounding box and reduce number of false and missed detections. The results indicate that the model's detection accuracy has increased by 5.94 percentage points due to the use of the improved algorithm. Moreover, ablation experiments are set to verify the impact of the proposed improved algorithm on the model performance. In the parameter performance comparison with other related algorithms, the parameter indicators perform well, proving the effectiveness and feasibility of the proposed algorithm.