• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computer Vision, IET >Object detection for panoramic images based on MS-RPN structure in traffic road scenes
【24h】

Object detection for panoramic images based on MS-RPN structure in traffic road scenes

机译：基于MS-RPN结构的交通道路场景全景图像目标检测

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

The objection detection of panoramic image is the key part of street view, intelligent transportation, automatic driving and other technologies. Due to the shortcomings of existing algorithms in detecting panoramic images, firstly a high-resolution panoramic image dataset is introduced, then the multi-scale feature pyramid networks (MS-RPN) structure is proposed and a new network with Sim-Inception module is designed. The network can extract different scales of objects from different feature layers, so that the small object in the image can also be accurately detected. Finally, the entire detection network is trained by using the dataset constructed in this study. Meanwhile, the ROIPool is replaced by ROIAlign and the loss function is adjusted according to the network structure. The experimental results show that the detection performance on the panoramic dataset is significantly improved by authors' proposed algorithm, which is better than other deep learning algorithms, especially for small object in the image.
机译：全景图像的异物检测是街景，智能交通，自动驾驶等技术的关键部分。针对现有算法在全景图像检测中的不足，首先介绍了高分辨率的全景图像数据集，然后提出了多尺度特征金字塔网络（MS-RPN）结构，并设计了一个新的带有Sim-Inception模块的网络。 。网络可以从不同的特征层中提取不同比例的对象，从而也可以准确检测图像中的小对象。最后，使用本研究构建的数据集训练整个检测网络。同时，将ROIPool替换为ROIAlign，并根据网络结构调整丢失功能。实验结果表明，作者提出的算法大大提高了全景数据集的检测性能，优于其他深度学习算法，特别是对于图像中的小物体。

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