[1]LIU Zhao,ZHANG Liming,GENG Meixiao,et al.Object detection of high-voltage cable based on improved Faster R-CNN[J].CAAI Transactions on Intelligent Systems,2019,14(4):627-634.[doi:10.11992/tis.201905026]
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Object detection of high-voltage cable based on improved Faster R-CNN
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
14
Number of periods:
2019 4
Page number:
627-634
Column:
学术论文—机器学习
Public date:
2019-07-02
- Title:
- Object detection of high-voltage cable based on improved Faster R-CNN
- Keywords:
- object detection; deep learning; high-voltage cable; complicated background; small object; live working; Faster R-CNN; region proposal
- CLC:
- TP18;TP391
- DOI:
- 10.11992/tis.201905026
- Abstract:
- The use of live working robots to replace human manual operation can effectively reduce the harm of a high-voltage and strong electric field to the human body and considerably improve the working efficiency. To solve the intelligent high-voltage cable object detection problem for live working robots under a complicated background environment, a high-voltage cable object detection method based on the improved Faster R-CNN is proposed. To improve the capability of extracting the high-level features of images in the network, skip connections are introduced and the order of the activation and convolution layers is adjusted. Then, the proposal bounding box generation mechanism is improved to enhance the performance of the proposed method for small object detection. Finally, the features of each region are extracted using the ROI pooling layers, and the classification and bounding box regression tasks are accomplished at the same time. Through the construction of high-voltage cable image datasets and the performance of numerous experiments based on the improved Faster R-CNN model, good accuracy and fast speed have been achieved.
- References:
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Last Update:
2019-08-25