Design and Evaluation of a Multiview Multispectral Photometric Stereo Instrument for Online Surface Defect Detection of Hot Continuous Casting Slabs

To address the technical challenges in online surface defects detection of hot continuous casting slab (HCCS), this paper presents the design, development, and validation of a novel vision-based instrumentation system based on Multi-View Multispectral Photometric Stereo (MVMSPS). The system integrates four cameras with specific spectral illuminators to intrinsically eliminate spectral crosstalk at inspection speeds up to 0.5 m/s. The core innovations are twofold: 1) A Generalized Homography Transformation (GHT) method is proposed for precise multi-view image matching, reducing geometric alignment errors by over 87.8% compared to conventional methods. 2) The optimal illumination zenith angle under the Lambertian model is theoretically deduced to guide the lighting design and maximize the signal-to-noise ratio. Enabled by these innovations, the MVMSPS algorithm effectively fuses multi-view and multispectral data to effectively estimate the surface normal and capture the high-frequency information of surface depth variations. Experimental results demonstrate that the proposed MVMSPS system can accurately, reliably, and efficiently detect surface defects (e.g., scars, cracks, and indentations) on HCCS in high-temperature and dynamic environments, with a complete processing time of under 3.6s per image set, satisfying the real-time requirements for online inspection and offering a promising instrument solution for intelligent production and quality control in the steel industry. Additionally, this paper explores potential future research directions, including combining fast two-dimensional detection with three-dimensional photometric stereo and integrating multimodal information, to further enhance detection performance.