In order to solve the problems of difficulty in defect identification, high rate of missed detection and false detection rate caused by factors such as irregular defect shape, variable size and wide variety of defects in photovoltaic cell defect detection, an improved photovoltaic cell defect detection algorithm based on YOLOv10n was proposed. Firstly, the bottleneck structure of the original C2f is eliminated, and the PMSFA_CSP module is designed as a partial feature extraction module of the backbone and neck network, and the ability to obtain context information through its partial multi-scale feature extraction and residual structure is designed to enhance the network′s ability to fuse defect features. Secondly, by using the shared convolutional layer with different expansion rates and the attention mechanism of SENetV2 aggregate dense layer, the FPSC_SENetV2 module is designed to introduce the backbone network to reduce local information loss and enhance the network′s ability to capture detailed features. Thirdly, FreqFFPN and PMSFA_CSP modules were fused, and the FreqFP_FPN modules were designed and feature pyramid networks were introduced to reduce category inconsistency and enhance the defect information of high-frequency details. Finally, the SESN loss function is constructed as the bounding box regression loss function to balance the detection of defects at different scales, accelerate the network convergence, and improve the computational efficiency. The experimental results show that compared with the original algorithm, the improved YOLOv10n is improved by 3.0%, the computational amount is reduced by 0.7 GFLOPs, and the parameter quantity is reduced by 0.08 M, which is compared with the original algorithm mAP@0.5, and the comprehensive performance meets the requirements of photovoltaic cell defect detection.