Paper Info
Title
An Efficient Detector For The Key Components Of The Power Transmission Lines
Abstract
Accurate detection of the key components of transmission lines is an important part of smart grid construction. However, the detection of key components of transmission lines faces the problems of severe occlusion, irregular shape, and large size differences, which present a great challenge for anchor-based object detectors. We propose the anchor-based and anchor-free (ABAF) model, an object detection algorithm for both general and special object datasets. The ABAF detector is jointly trained with the anchor-based branch and anchor-free branch. At the time of inference, the predicted results of both are fused to yield the final detections. The experimental results show that the anchor-based branch is good at detecting shape-regular objects and the ancho-rfree branch is better at detecting irregularly shaped objects than anchor-based branch, and our fusion model ABAF has strong robustness for different datasets with excellent and stable performance. ABAF with ResNet-50 achieves 89.69% mAP on the transmission line dataset, a 3.72% improvement over one-stage anchor-based detector RetinaNet and we achieve 79.03% mAP on the PASCAL VOC dataset.
Year
DOI
Venue
2020
10.1109/ICTAI50040.2020.00140
2020 IEEE 32ND INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI)
Keywords
DocType
ISSN
transmission line, key components, object detection, anchor-free detector, deep learning
Conference
1082-3409
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Xiangcheng Liu100.34
Qingzhou Dong200.34
Youjun Xiang342.09
Yuli Fu420029.90