| 叶 飞,骆星智,宋永春,丁国成,杨孝志,谭守标.基于双特征融合的改进 R-CNN 电力小金具缺陷检测方法研究[J].电子测量与仪器学报,2023,37(7):213-220 |
| 基于双特征融合的改进 R-CNN 电力小金具缺陷检测方法研究 |
| Research on defect inspection of power small fittings based on improved R-CNN and double feature fusion |
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| DOI： |
| 中文关键词: 电力小金具缺陷识别 深度学习算法 卷积神经网络 Faster R-CNN 特征提取 目标检测 |
| 英文关键词:defect identification of small electric power fittings deep learning algorithm convolution neural network Faster R-CNN feature extraction object detection |
| 基金项目:国网安徽省公司科技项目(B31206220005)资助 |
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| 摘要点击次数: 1042 |
| 全文下载次数: 2006 |
| 中文摘要: |
| 电力金具作为输电线路中的不可缺少的关键部件,对电力稳定传输提供了保障,一旦电力金具出现缺陷,就会带来巨大
的隐患,造成输电设施的损坏甚至大面积停电事故,影响人们的生产和生活。 传统的输电线路检修主要依靠人工现场进行巡
检,不仅危险程度高,辨识难度也比较大。 人工智能识别技术的不断进步,为电力金具的缺陷识别提供了更好的方法。 目前
Faster-RCNN 算法的目标识别准确率高,但对于螺钉等小金具目标物体的识别率相对较低。 本文首先通过双特征融合算子提取
特征并进行标记后,输入引进混合注意力机制改进的 Faster R-CNN 模型中,进行特征再提取,融合重合度较高的特征,并进行缺
陷的分类和识别,能够对电力小金具中的螺钉进行高效的辨识。 实验结果表明,本文双特征融合的改进 Faster R-CNN 模型相较
于传统的 Faster R-CNN 模型和 YOLO 模型的提升效果明显,模型的平均准确率提升了 5%,平均精度提升了 11%,在保障算法
实时性的同时对螺钉等电力小金具具有较好的检测效果。 |
| 英文摘要: |
| As an indispensable key component of power transmission lines, power fittings provide a guarantee for stable power
transmission. Once the electric power fittings have defects, it will bring huge hidden dangers, causing damage to transmission facilities
or even large-scale power failure, affecting people’ s production and life. The traditional power transmission line maintenance mainly
depends on manual on-site maintenance, which is not only dangerous, but also difficult to detect. The continuous progress of AI
recognition technology provides a better method for the defect recognition of electric power fittings. At present, the target recognition
accuracy of Faster R-CNN is high, but it is relatively low for small target objects such as screws. Firstly, the features are extracted and
marked by the double feature fusion operator, then input into the improved Faster R-CNN model with the introduction of mixed attention
mechanism for feature re extraction. The features with high coincidence degree are fused, and the defects are classified and recognized,
which can effectively identify the screws in the small power fittings. The experiment shows that the improved Faster R-CNN based on dual
feature fusion in this paper has obvious improvement effect compared with the traditional Faster R-CNN and YOLO. The average accuracy
of the model is improved by 5%, and the average accuracy is improved by 11%, which also ensures the real-time performance of the
algorithm identification. It has a good detection effect on small electrical fittings such as screws. |
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