<p>Dark-line defects in the active region of vertical-cavity surface-emitting lasers (VCSELs) reduce optical power and compromise system stability. Therefore, it is of paramount importance to identify these defects, which can help improve production processes. Traditional methods rely on visual inspection or electrical characteristics, requiring specialized knowledge. We propose an end-to-end defect segmentation network with a novel dual-encoder mechanism to extract both local and global features of defects. In response to the challenges posed by random sizes and varying shapes of defects, we propose a dynamic convolution encoder (DCE) to extract local features. To capture the overall distribution of defects, we present the Shifted MLP Encoder (SME) to extract global context information. A hybrid loss function is employed to improve the ability to identify defect samples to address the imbalance between positive and negative samples. Additionally, deep supervision is applied to oversee multi-scale features in intermediate layers, enhancing segmentation performance. Finally, experimental results on the VCSEL dataset we made demonstrate that the dice similarity coefficient (DSC) and mean intersection over union (mIoU) of defect segmentation reach 90.72% and 91.58%, respectively. Compared with other advanced methods, our proposed method improves segmentation accuracy with only a slight increase in computational complexity.</p>