首页|改进的Faster R-CNN绝缘子识别模型

改进的Faster R-CNN绝缘子识别模型

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为了适应更多更复杂的传输线路巡检要求,提高巡检实时性,提出了一种基于改进的Faster R-CNN绝缘子目标检测算法模型。对于拍摄传输的巡检绝缘子图像,提出了一种小波变换融合维纳滤波的去噪方法。针对绝缘子的形状特征,将原有的锚框比例1∶1,1∶2,2∶1改为更加适合研究数据的1∶2,2∶1,1∶3,3∶1,并用软化非极大值抑制算法代替原有的非极大值抑制算法解决因拍摄角度而存在的绝缘子遮挡的漏检情况。实验结果表明,改进的Faster R-CNN目标检测算法比原有的算法有更高的识别准确率。
Improved Faster R-CNN Insulator Recognition Model
In order to meet the requirements of more complex transmission line inspection and improve the real-time perfor-mance of inspection,an improved algorithm model of Faster R-CNN insulator target detection is proposed.A wavelet transform fu-sion wiener filter method is proposed for denoising the patrol insulator images.According to the shape and characteristics of insula-tors,the original anchor frame ratios of 1∶1,1∶2 and 2∶1 are changed to 1∶2,2∶1,1∶3 and 3∶1,which are more suitable for the re-search data,and the softening non-maximum suppression algorithm is used to replace the original non-maximum suppression algo-rithm to solve the problem of insulator occlusion missing due to the shooting angle.Experimental results show that the improved Fast-er R-CNN target detection algorithm has higher recognition accuracy than the original algorithm.

Faster R-CNNobject detectioninsulatorwavelet transformWiener filter

郝旭龙、廖金、董国芳

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云南民族大学电气信息工程学院 昆明 650504

Faster R-CNN 目标检测 绝缘子 小波变换 维纳滤波

国家自然科学基金项目

61662089

2024

计算机与数字工程
中国船舶重工集团公司第七0九研究所

计算机与数字工程

CSTPCD
影响因子:0.355
ISSN:1672-9722
年,卷(期):2024.52(2)
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