首页|基于改进Faster R-CNN的绝缘子缺陷检测识别与定位

基于改进Faster R-CNN的绝缘子缺陷检测识别与定位

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针对现有算法对绝缘子检测精度不高的问题,在 Faster R-CNN 算法的基础上进行改进,利用检测效果更好、性能更优的 ResNet50 代替原始 VGG 网络进行缺陷识别.实验结果表明,改进算法在数据集上的 mAP 达到77.29%,召回率达到 87.55%,与其他经典算法相比具有更好的准确性与较强的实时性.
Insulator Defect Detection,Identification and Location Based on Modified Faster R-CNN
In view of the problem of unsatisfactory accuracy of existing algorithm in insulator detection,the present work made an improvement by using Faster R-CNN algorithm,and introduced Resnet50,which has better detection utility and excellent performance,to replace the original VGG network for defect identification.The improved algorithm,according to experimental results,can achieve a mAP reaching 77.29%on the data set in this paper,and a recall rate of 87.55%,exhibiting better accuracy and stronger real-time performance compared with other typical algorithms.

insulatoraccuracyreal-timeFaster R-CNNResnet network

贺元帅、纪超、王博雅、贾星海、张凡、李小兵

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西安工程大学,陕西 西安 710048

绝缘子 准确性 实时性 FasterR-CNN Resnet网络

2024

电工技术
重庆西南信息有限公司(原科技部西南信息中心)

电工技术

影响因子:0.177
ISSN:1002-1388
年,卷(期):2024.(1)
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