A defect detection algorithm OM-Detector based on reparameterization was proposed to solve the problems of uneven background interference, variable shape and multi-scale defects in electroluminescence image of photovoltaic cells. Firstly, OREPANCSPELAN4 module is proposed by combining generalized high-efficiency layer aggregation network and online reparameterization. The introduction of heavy parameterization can effectively train through gradient descent optimization algorithm, which can improve the accuracy and reduce the number of model parameters, making the model lightweight. Secondly, a multi-scale convolutional attention module is introduced into the neck network to suppress the interference of complex background and improve the accuracy of the model to detect fine defects. Finally, a defect detector is constructed by combining the heavy parametric feature extraction-fusion module and the multi-scale convolution attention module. The performance of the algorithm was verified by using the photovoltaic cell anomaly detection data set. The experimental results showed that compared with the YOLOv8 detection network, the mean average precision was increased by 2.5%, the number of parameters was reduced by 29%, and the reasoning speed was accelerated by 5.7%, which was superior to the current mainstream target detection algorithm and could detect the surface defects of photovoltaic cells quickly and accurately.