[1]赵振兵,江爱雪,戚银城,等.嵌入遮挡关系模块的SSD模型的输电线路图像金具检测[J].智能系统学报,2020,15(4):656-662.[doi:10.11992/tis.202001008]
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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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]
嵌入遮挡关系模块的SSD模型的输电线路图像金具检测
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
15
期数:
2020年第4期
页码:
656-662
栏目:
学术论文—智能系统
出版日期:
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
- 分类号:
- TP18;TN911.73
- 摘要:
- 为了提升深度学习目标检测模型在输电线路金具自动化检测任务中的准确率，针对金具检测数据集中金具目标标注框之间不可避免地广泛存在相交而导致金具目标检测定位不准确的问题，本文利用相交区域的相似性作为金具目标的上下文信息，提出目标间遮挡关系的描述方法，用于规则性描述图像中金具目标间的相互遮挡，设计遮挡关系模块，并将其嵌入到单次多框检测器(single shot multibox detector, SSD)模型中。为了验证嵌入遮挡关系模块的SSD模型的有效性，选择了8类目标标注框普遍存在相交的小目标金具进行实验，实验使用的金具检测数据集的训练集和测试集中金具目标数分别为6 271和1 713。实验证明，原始SSD模型的平均精度均值(mean average precision, mAP)为72.10%，嵌入遮挡关系模块的SSD模型的mAP为76.56%，性能提升了4.46%。
- 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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备注/Memo
收稿日期:2020-01-06。
基金项目:国家自然科学基金项目（61871182，61773160）；北京市自然科学基金项目（4192055）；河北省自然科学基金项目（F2020502009）；中央高校基本科研业务费专项资金项目（2018MS095，2020YJ006）；模式识别国家重点实验室开放课题（201900051）；国家留学基金项目（201906735011）
作者简介:赵振兵，副教授，博士，主要研究方向为电力视觉。主持国家自然科学基金等纵向课题10项；获河北省科技进步一等奖1项。以第一完成人获得国家专利授权16项。发表学术论文30余篇，出版专著2部;江爱雪，硕士研究生，主要研究方向为电力目标检测与深度学习;赵文清，教授，博士，主要研究方向为人工智与数据挖掘。发表学术论文50余篇
通讯作者:赵振兵.E-mail:zhaozhenbing@ncepu.edu.cn
更新日期/Last Update:
2020-07-25