To address the issue where minor surface defects of IC devices are often obscured by redundant noise in traditional pixel-level fusion detection—hindering defect feature extraction—and the challenge of adaptively adjusting the contribution of visible and infrared images in complex detection scenarios with unstable lighting, this paper proposes a surface defect detection method for IC devices based on multispectral image feature fusion. The method employs a mid-fusion strategy to design a Multispectral Image Feature Fusion (MIFF) module and establishes a dual-path feature extraction channel within the YOLO framework. This leads to the development of an end-to-end YOLO-MIFF defect detection model specifically for multispectral image feature fusion. Experimental results demonstrate that the YOLO-MIFF fusion detection model achieves a mean Average Precision (mAP) that is 24.69% and 35.65% higher than that of single visible and single infrared image detection, respectively. Additionally, compared to the YOLO-Multiply, YOLO-Concat, and YOLO-Add models, YOLO-MIFF improves detection accuracy by 9.85%, 6.67%, and 3.44%, respectively.