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Semantic Segmentation Optimization in Power Systems: Enhancing Human-Like Switching Operations

Jin Hua,Yue Zhao, Huijun Zhang, Haiming Zhao,Lei Wang

Traitement du Signal(2023)SCI 4区

Xian Technol Univ

Cited 0|Views9
Abstract
In addressing the digital and intelligent transformation challenges within the traditional desulfurization process of coal secondary utilization, a perception learning model rooted in semantic segmentation networks has been developed. This model, when integrated with real-world operational environments, is shown to confront issues arising from symmetry and multi-scale features inherent to thermal power distribution room contexts. A combination of multi-scale feature fusion and attention mechanisms has facilitated the precise detection of human-like operation knobs on switch operation panels. To cope with extended dynamic scenes, a method relying on the visual bag-of-words has been adopted, wherein local image features are extracted and matched against a visual dictionary, resulting in a refined visual representation. The subsequent selection of consecutive symmetrically similar scene keyframes and the elimination of superfluous data have been observed to augment the efficacy of loop-closure detection. Such enhancements have culminated in improved accuracy in the SLAM (Simultaneous Localization and Mapping) of mobile robots, enabling their autonomous navigation to designated switching operation task locations. Experimental findings underscore the superiority of this optimized model over traditional semantic segmentation networks, with its ability to pinpoint operation knobs on electrical control cabinet panels in distribution rooms. Moreover, before initiating grasping actions under the Eye-in-hand architecture, visual servo grasping maneuvers can be executed, irrespective of the target's appearance angle within the field of view. This optimization offers an insightful foundation for potential integrations into patrol operation mobile robots, marking a feasible and effective stride forward.
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semantic segmentation,attention mechanism,multi-scale fusion,visual bag-of-words,SLAM,digital transformation
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要点】:本研究针对火电厂脱硫过程中的智能化转型挑战,提出了一种基于语义分割网络的感知学习模型,通过多尺度特征融合和注意力机制优化了人形操作开关的检测,提高了移动机器人在SLAM中的定位与建图精度。

方法】:研究采用多尺度特征融合与注意力机制相结合的方法,并利用视觉词袋模型处理动态场景,通过选择连续对称关键帧和消除冗余数据来增强闭环检测效果。

实验】:实验在真实火电厂环境中进行,使用的数据集为特定分布室场景,结果显示优化后的模型在识别操作面板上的开关操作旋钮方面优于传统语义分割网络,且在Eye-in-hand架构下能够进行视觉伺服抓取操作,不受目标物角度影响。