Vision-based measurements (VBM) technology has been widely applied in the quality monitoring of various products. However, most of the existing studies only focus on the defect detection methods using a single-modal image (RGB image or Thermal Infrared image). In order to detect surface and internal defects more comprehensively, this article provides a potential defect detection technology introducing the RGB-Thermal infrared salient object detection (RGB-T SOD) into VBM. A novel information flow fusion network (IFFNet) method is proposed for the RGB-T cross-modal images. The proposed IFFNet consists of an information filtering module and a novel information flow paradigm. Validation on three available RGB-T SOD datasets shows that our proposed method performs more competitively than the state-of-the-art (SOTA) methods. For the key evaluation metric W_F, the experimental results of our method are 0.849, 0.912, and 0.856 on VT821, VT1000, and VT5000 datasets, respectively.