Aiming at the challenges of small industrial defect targets, blurred boundaries and complex backgrounds, this paper proposes a lightweight industrial defect detection network named MSCENet based on multi-scale context enhancement. First, a multi-scale attentionenhanced feature extraction module （MAFE） is introduced, which employs parallel multi-scale dilated convolutions to capture defect features under varying receptive fields. Next, a residual enhancement fusion （REF） module is designed to adaptively integrate multi-level features using a dual-attention feature enhancement mechanism alongside residual connections, thereby improving the reconstruction quality of defect boundaries and details in the decoder. Furthermore, a global attention aggregation （GAA） module is proposed to focus on defect regions while suppressing background interference, further enhancing detection accuracy and robustness. Experimental results on three industrial defect datasets demonstrate that, compared to the backbone network FasterNet-T1, the proposed method achieves significant improvements in mean intersection over union （IoU）, mean pixel accuracy, and overall accuracy, with only 9.418 million parameters, while improving industrial inspection efficiency.