Affiliation:
1. Jiangsu Frontier Electric Technology, Nanjing 211102, China
2. Beijing Imperial Image Intelligent Technology, Beijing 100085, China
Abstract
Deep learning technology has received extensive consideration in recent years, and its application value in target detection is also increasing day by day. In order to accelerate the practical process of deep learning technology in electric transmission line defect detection, the current work used the improved Faster R-CNN algorithm to achieve data-driven iterative training and defect detection functions for typical transmission line defect targets. Based on Faster R-CNN, we proposed an improved network that combines deformable convolution and feature pyramid modules and combined it with a data-driven iterative learning algorithm; it achieves extremely automated and intelligent transmission line defect target detection, forming an intelligent closed-loop image processing. The experimental results show that the increase of the recognition of improved Faster R-CNN network combined with data-driven iterative learning algorithm for the pin defect target is 31.7% more than Faster R-CNN. In the future, the proposed method can quickly improve the accuracy of transmission line defect target detection in a small sample and save manpower. It also provides some theoretical guidance for the practical work of transmission line defect target detection.
Subject
Computer Science Applications,Software
Reference30 articles.
1. Target detection method based on convolutional neural network for SAR image;L. Du;Journal of Electronics Information Technology,2016
2. Adaptive windows multiple deep residual networks for speech recognition
3. Evaluation of Power Insulator Detection Efficiency with the Use of Limited Training Dataset
4. Deep learning based multi-category object detection in aerial images;L. W. Sommer
5. Deep learning architectures for underwater target recognition;S. Kamal
Cited by
15 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献