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At present, clothing thermal resistance is mainly measured by manual questionnaire, which requires the subjects to fill in the questionnaire many times. The estimation process is complex and not easy to measure in real time. Moreover, the traditional estimation method only predicts the static thermal resistance, and does not consider the influence of personnel movement state and indoor wind speed. In the aspect of image detection, the clothing detection method based on mask RCNN network has the problems of multi-scale feature information loss and poor fusion. In view of these situations, an improved mask RCNN clothing detection network method is proposed to apply and realize the system design of indoor personnel dynamic clothing thermal resistance. Firstly, the CCD camera is used for image acquisition, and the improved mask RCNN network is used to detect the amount of clothing. Then, look-up table mapping method is used to estimate the thermal resistance of indoor clothing. Finally, the wind speed and walking speed measured by the measuring instrument are used to correct the thermal resistance of clothing, and the estimation result of dynamic thermal resistance of clothing is obtained. The experimental results show that the average recognition accuracy of the improved mask RCNN network is 1.1% higher than that of the original method. Compared with the traditional method, the improved mask RCNN network can correct the average deviation of 0.13.