To enhance the accuracy of photovoltaic cell defect detection in complex scenarios such as multiple categories and small targets, this paper proposes a lightweight improved model based on RT-DETR. Firstly, a lightweight dual-path feature extraction module was designed to replace the basic module of ResNet. This not only reduced the model parameters but also enabled the model to possess the ability of local and global feature modeling. Secondly, the multi-head attention module in (attention-based intra-scale feature interaction，AIFI） was improved by deleting some attention heads and performing linear transformations on the remaining attention heads to alleviate the redundancy problem in the multi-head attention mechanism. Finally, the convolutional downsampling module in the network was replaced by a downsampling module based on wavelet transformation to improve the problem of edge information loss during downsampling. The experimental results show that the number of parameters of the improved model has decreased by 47.3% compared to the original model, and the computational cost has decreased by 39.7%. On the private dataset, the improved model outperformed the baseline with increases of 2.2%, 4.1% and 2.4% in precision, recall and mAP@50, respectively, demonstrating its effectiveness. In addition, a generalization experiment was conducted on the public PVEL-AD dataset, where the improved model achieved an mAP@50 of 70.6%, which is 6.3% higher than the baseline model, providing initial evidence of its generalization ability on unseen data.