A Two-stage Detection Algorithm for Workwear Compliance in Power Construction Scenarios Based on Feature Guidance

Abstract

In the existing electric construction scenario， the intelligent supervision scheme for wearing workwear focuses on safety helmets， and there are few schemes for the overall workwear compliance of operators. In view of the more detailed requirements of workers′ workwear compliance supervision， a two-stage compliance detection algorithm is proposed， which includes the personnel positioning stage and the human body area workwear compliance detection stage. Given the complex working posture of field personnel， a personnel positioning algorithm integrating FPN （feature pyramid network） and Guided Anchor based on Faster R-CNN is proposed. On the data set composed of more than 15,000 on-site collected samples， a personnel positioning accuracy of 91.11% is obtained， 6.0% higher than that of the ordinary Faster R-CNN scheme. The two-stage detection algorithm achieves an accuracy of 92.9% in the human body area workwear detection task， which is 11.4% higher than the single-stage Faster R-CNN scheme.

Keywords

• wearing compliance
• guided anchor
• two-stage detection
• personnel positioning
• human body area workwear compliance detection