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知识推理和渐进蒸 - 电气施工违规行为的综合检测
Bin Ma1, Gang Liang1, Yufei Rao1
1State Grid Henan Electric Power Company Electric Power Science Research Institute, Zhengzhou 450052, China.
本研究引入了一种使用知识图和蒸来检测电力建设中工人违规行为的新方法. 优化的模型显著减少了尺寸和计算,同时提高了检测准确度,以提高现场安全性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 建筑工程安全 建筑工程安全
背景情况:
- 在电力建设中发现工人违规行为是具有挑战性的.
- 现有的方法在复杂的场地环境中可能缺乏准确性和效率.
研究的目的:
- 开发一种创新的方法,以准确检测电力建设中的工人违规行为.
- 提高安全监控系统的效率和准确性.
主要方法:
- 从电力建设标准和规范构建了一个知识图.
- 将知识图与使用三胞胎和自适应连接网络的物体检测模型集成.
- 实施了一种渐进的多层次蒸策略,使用两种教师模型进行知识转移.
主要成果:
- 模型大小从14.5 MB减少到3.8 MB,FLOP从15.8 GFLOP减少到5.9 GFLOP.
- 实现了92.4%的AP50,比原始模型提高了1.8%.
- 在蒸过程中显著减少了上下文信息丢失.
结论:
- 拟议的方法有效地检测工人违规行为,提高了准确性和效率.
- 为建设工地加强安全管理和监控提供了一种新的方法.
- 为该方法在资源有限的环境中的优越性提供了定量基础.
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