Abstract: To address the issues of high complexity and insufficient real-time performance in existing deep learning models for photovoltaic （PV） module surface defect detection, this paper proposes a surface defect detection algorithm integrating lightweight architecture and hierarchical knowledge distillation. Based on the CenterNet framework, the algorithm introduces a dual-path feature pyramid network with a bidirectional interactive feature which could transfer mechanism to enhance feature fusion efficiency. An improved Gaussian kernel generation strategy is further developed, which employs a radiation-based Gaussian kernel coverage centered on target points to optimize the loss function, thereby increasing the network's sensitivity to defect localization. Additionally, a hierarchical knowledge distillation scheme is proposed, enabling a two-stage knowledge transfer from complex to simplified models to resolve inefficiencies caused by architectural discrepancies between teacher and student networks. Experimental results demonstrate that the proposed algorithm achieves an average precision of 83.7%, and model parameters are reduced to 5.7M, and a real-time detection speed of 143.4 frames per second （FPS） is attained. This approach effectively balances detection accuracy and computational efficiency, meeting the requirements for online inspection in PV production lines.