To address the issue of low defect detection accuracy in IC devices due to insufficient contrast under either visible light or infrared conditions alone, this paper introduces a multi-spectral fusion approach. Initially, to overcome scale inconsistency and contrast inversion challenges during IC device image registration, we enhance the ORB (Oriented FAST and Rotated BRIEF) algorithm with a Laplacian pyramid and feature descriptor recombination strategy. Following image registration, we propose the NSST_VP image fusion method, which processes the infrared and visible images' low and high frequency subbands through Non-Subsample Shearlet Transform (NSST). For fusion, the low frequency subband uses a visual significance map (VSM) weighted rule, and the high frequency subband employs a PA-Pulse Coupled Neural Network (PA-PCNN) decision rule, with the final image produced by reversing the NSST. The fused image is then analyzed using the YOLOv8s model. Experimental findings reveal an 87.8% average accuracy with the improved ORB registration, marking a 62% enhancement over the standard ORB. The NSST_VP fusion algorithm significantly boosts both subjective and objective metrics, achieving an mAP of 83.15%-surpassing single light mode detections by 22.97% and 28.31%, and outperforming Dual-Tree Complex Wavelet, Non-Subsampled Contourlet, and Curvelet Transform fusion methods by 13.14%, 15.01%, and 20.35%, respectively.