People’s behavior in industrial inspections is closely bound up with safe production, and the design of inspection monitoring methods has become a hot research area. Aiming at the problem that current monitoring and analysis of inspection depend on manual judgment with low accuracy, this study proposes a monitoring and analysis system for industrial inspection based on machine vision. Firstly, the people in the video stream are detected by the YOLOv3 network. According to the detection results, in-scene interferences are removed by behavior analysis to obtain real behavior of inspectors. Finally, the inspection process is evaluated based on the behavior, and then results are stored in the database and posted to the web page. Videos with multiple monitoring perspectives are used for experiments. Results demonstrate that the system proposed in this study can accurately detect inspectors and analyze their behavior in complex environments, while achieving real-time processing. This result can serve as a reference for the intelligent monitoring of industrial inspection.