Abstract: To accurately capture small defect information in insulators, improve the reconstruction quality of terahertz images, and achieve precise insulator defect detection, a method for insulator defect detection based on terahertz pulse time-domain feature reconstruction algorithm was studied. This method combined improved Hilbert Huang transform and ensemble empirical mode decomposition to perform deep analysis of terahertz time-domain signals of insulators and accurately extract defect features. Multi-dimensional features were effectively fused through principal component analysis, and they were mapped to an orthogonal basis feature space using coordinate transformation techniques, a two-dimensional terahertz image was reconstructed that contain rich feature information of insulator defects. Furthermore, the reconstructed image was input into the YOLOv3 based insulator defect detection model, where the Darknet-53 feature extraction network was used to capture defect features, achieving intelligent defect detection. The experimental results showed that the proposed method can effectively improve the quality of generated terahertz images of insulators and these images were rich in defect feature. The proposed method achieved a recognition rate consistently above 0.95 and could reach up to 0.98, demonstrating high detection accuracy.