A Study on the Applicability of Computer Vision Technology in the Field of Engineering Management

With the development of big data and deep learning technologies, computer vision has emerged as a critical enabler for intelligent transformation in construction engineering management. However, challenges persist in algorithm accuracy, scenario adaptability, and privacy protection. This study systematically evaluates the applicability of computer vision in construction management through literature reviews, case analyses, and industry surveys. Findings reveal that computer vision has demonstrated significant efficacy in quality control, safety management and progress monitoring. Nevertheless, technical limitations include high data dependency, insufficient multimodal integration, and weak adaptability to dynamic environments. Surveys of industry practitioners highlight strong recognition of the technology’s potential in safety monitoring, defect quantification and equipment efficiency analysis. Main obstacles to the application of technology include a shortage of skilled personnel, cost inefficiency and privacy concerns. The study concludes that computer vision exhibits strong applicability in complex and high-precision scenarios but requires breakthroughs in dynamic environment adaptation, 3D scene understanding and privacy safeguards. Future directions emphasize adaptive model optimization, multimodal data fusion and integration with systems like BIM and Digital Twins. Through technological refinement and cross-sector collaboration, computer vision is poised to drive intelligent and sustainable advancements in construction management.