[1]ZHAO Zhenbing,JIANG Aixue,QI Yincheng,et al.Fittings detection in transmission line images with SSD model embedded occlusion relation module[J].CAAI Transactions on Intelligent Systems,2020,15(4):656-662.[doi:10.11992/tis.202001008]
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Fittings detection in transmission line images with SSD model embedded occlusion relation module
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
15
Number of periods:
2020 4
Page number:
656-662
Column:
学术论文—智能系统
Public date:
2020-07-05
- Title:
- Fittings detection in transmission line images with SSD model embedded occlusion relation module
- Keywords:
- transmission line fittings; occlusion; occlusion relationship description; occlusion relationship module; single shot multibox detector; groundtruth box; object detection; deep learning
- CLC:
- TP18;TN911.73
- DOI:
- 10.11992/tis.202001008
- Abstract:
-
In order to improve the accuracy of the deep learning object detection model in the automatic detection of transmission fittings, aiming at the problem of inaccurate detection and location of fittings due to the inevitable extensive intersection between the groundtruth boxes of fittings in the fittings dataset, this article proposes a description method of the occlusion relation between the objects, so as to regularly describe the mutual occlusion between the objects by using the similarity of the intersection area as the context information of the fittings. The occlusion relation module is designed and embedded in the single shot multibox detector (SSD) model. In order to verify the effectiveness of the SSD model embedded with the occlusion relation module, eight kinds of small objects with intersecting groundtruth boxes are selected for experiments, and the object number of the training set and the test set of the fittings dataset used in the experiment is
6271 and1713 respectively. The experiments show that the mean average precision (mAP) of the original SSD model is 72.10%, the mAP of the SSD model embedded in the occlusion relation module is 76.56%, and the performance is improved by 4.46%.
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Last Update:
2020-07-25