To address the issues of high missed detection rates for small targets, insufficient robustness against complex background interference, and limited cross-scale defect detection capabilities in existing target detection methods for photovoltaic cell electroluminescence images, a defect detection model based on MFES-YOLOV8n is proposed to enhance detection accuracy and efficiency in industrial scenarios. First, a C2f-ST feature extraction module is embedded into the backbone network, utilizing the window-based self-attention mechanism of Swin Transformer to enhance local-global feature associations for micro-defects, combined with residual connections to preserve shallow-layer detail features. Therefore, the fine-grained feature extraction capabilities are improved. Secondly, an ES-SPPCSPC feature representation module is designed, integrating group convolution with an enhanced SimAM attention mechanism, achieving dynamic suppression of background noise and enhancement of defect-specific features through synergistic optimization of energy-based, channel, and spatial attention. Finally, an MSFF-Neck multi-scale feature fusion module is established, employing scale-sequential feature fusion and triple feature encoding strategies to enable complementary interactions between deep semantic and shallow detail features, mitigating multi-scale feature degradation. Experiments on the PVEL-AD dataset validate the model’s effectiveness. Results show that it achieves an mAP@0.5 of 0.897 with 6.1 M parameters, improving by 3.0% over the baseline YOLOv8n. Through a progressive optimization strategy of “fine-grained feature extraction, cross-scale semantic enhancement, and multi-level feature fusion”, this study overcomes performance bottlenecks in multi-category and cross-scale defect detection of traditional models, providing a high-precision, lightweight, and edge-computing-compatible defect detection solution for industrial scenarios. While maintaining low computational complexity, it meets the demands for real-time performance and reliability in industrial applications, offering technical support for advancing quality control and intelligent maintenance in the photovoltaic industry.