...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Robotics & Automation >DETECTION OF TRANSMISSION LINE AGAINST EXTERNAL FORCE DAMAGE BASED ON IMPROVED YOLOv3
【24h】

DETECTION OF TRANSMISSION LINE AGAINST EXTERNAL FORCE DAMAGE BASED ON IMPROVED YOLOv3

机译：基于改进的YOLOV3的外力损伤检测输电线路

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

With the continuous acceleration of the infrastructure construction process, the emergence of a large number of engineering vehicles poses a great threat to the power transmission lines, and the detection of engineering vehicles around transmission lines has become one of the essential measures to guarantee the safety of transmission lines. Therefore, an engineering vehicle detection model around the transmission lines is proposed based on the improved YOLOv3. First of all, the training dataset is enhanced through data augmentation, and the number of images between classes is more balanced. Next, the elbow method is used to determine the number of anchors, and the k-means++ algorithm is used to cluster the dataset to determine the size of anchors. Then, to strengthen the fusion of features, the high-level features are cascaded to the low-level features, while the low-level features are also cascaded to the high-level features. Finally, the channel attention mechanism and the spatial attention mechanism are cascaded to enhance the information of the features. Experiments show that the improved model is better than the Faster-RCNN, single shot multibox detector (SSD) and YOLOv3. Its mean average precision (mAP) value is improved by nearly 7% compared with YOLOv3. This method can be used well for the detection of engineering vehicles.
机译：随着基础设施施工过程的持续加速，大量工程车辆的出现构成了对电力传输线的巨大威胁，并且传输线周围的工程车辆的检测成为保证安全的必要措施之一传输线。因此，基于改进的YOLOV3提出了传输线周围的工程车辆检测模型。首先，通过数据增强增强训练数据集，类之间的图像数量更加平衡。接下来，使用弯头方法来确定锚点的数量，并且k平均++算法用于聚类数据集以确定锚的大小。然后，为了增强功能的融合，高级功能级联到低级功能，而低级功能也级联到高级功能。最后，级联机制和空间注意机制级联以增强特征的信息。实验表明，改进的模型优于更快的RCNN，单次射击多杆探测器（SSD）和YOLOV3。与Yolov3相比，其平均平均精度（MAP）值近7％提高了近7％。这种方法可以很好地用于检测工程车辆。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号