A Survey on Camera-Based Measurement Systems: Techniques, Applications, and Challenges

Camera-based measurement systems have become indispensable across engineering and scientific domains, offering non-contact, high-precision quantification of physical phenomena. This review analyzes their principles, applications, and challenges, revealing that modern systems achieve remarkable accuracy (micron-scale in surface metrology, sub-centimeter in environmental monitoring) but face fundamental trade-offs. While traditional image processing enables efficient edge deployment (60 fps), deep learning methods deliver superior accuracy (98.86%) at a higher computational cost. Persistent challenges include environmental sensitivity (15-25% accuracy degradation outdoors), calibration complexity (15-30 min/setup), and processing bottlenecks. Emerging solutions like multi-spectral imaging (18-22% robustness improvement) and hybrid architectures (40% power reduction) point toward next-generation adaptive systems. The findings provide both a state-of-the-art reference and a roadmap for overcoming current limitations in optical metrology.