Aiming to address the false detections caused by background reconstruction errors in the self-supervised leaky cable snap defect detection algorithm, this paper proposes a two-stage leaky drop snap defect reconstruction method based on region of interest constraints. For the classified snap region images localized by the target detection algorithm, a segmentation network is first employed to differentiate between snap and cable leakage regions. Subsequently, the corresponding masks are utilized to guide a stacked adversarial generative network to reconstruct the snap regions, ensuring high-quality reconstruction of the defect areas while maintaining background consistency. Additionally, the generative network is optimized to place greater emphasis on reconstructing the regions of interest by integrating deep residual blocks and refining the loss function. Ultimately, the trained network is deployed for the reconstruction of snap images, determining the presence of defects based on the similarity scores of the images before and after reconstruction. Quantitative results on the leaky cable snap dataset demonstrate that the proposed algorithm achieves a defect recognition accuracy of 92.3% and a recall rate of 93.4%, surpassing the performance of other self-supervised snap reconstruction methods. Visualization results further indicate a reduction in background reconstruction errors in the proposed method.