...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Procedia Computer Science >Real-time object detection based on YOLO-v2 for tiny vehicle object
【24h】

Real-time object detection based on YOLO-v2 for tiny vehicle object

机译：基于YOLO-V2对于微小车辆对象的实时对象检测

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In Automatic Driving System (ADS) and Driver Assistance System (DAS), object detection plays a vital part. Nevertheless, existing real-time detection models for tiny vehicle objects have the problems of low precision and poor performance. To solve these issues, we propose a novel real-time object detection model based on You Only Look Once Version 2 (YOLO-v2) deep learning framework for tiny vehicle objects, called Optimized You Only Look Once Version 2 (O-YOLO-v2). In the proposed model, a new structure is introduced to strengthen the feature extraction ability of the network by adding convolution layers at different locations. Meanwhile, the problem of gradient disappearance or dispersion caused by increasing network depth is solved by adding residual modules. Furthermore, in order to promote the detection accuracy of tiny vehicle objects, we combine the low-level features and high-level features of the network. The experimental findings and analysis on a KITTI dataset show that the model not only promotes the accuracy of tiny vehicle object detection but also improves the accuracy of vehicle detection (the accuracy reaches 94%) without decreasing the detection speed.
机译：在自动驱动系统（广告）和驾驶员辅助系统（DAS）中，对象检测扮演一个重要的部分。尽管如此，对于微小的车辆物体的现有实时检测模型具有低精度和性能差的问题。要解决这些问题，我们提出了一种新颖的实时对象检测模型，仅适用于一旦版本2（YOLO-V2）深入学习框架，用于微小的车辆对象，称为优化，您只需看一次版本2（O-Yolo-V2 ）。在所提出的模型中，引入了一种新的结构，通过在不同位置添加卷积层来增强网络的特征提取能力。同时，通过添加残留模块来解决通过增加网络深度引起的梯度消失或色散的问题。此外，为了促进微小车辆对象的检测精度，我们将低级功能和网络的高级功能组合。基蒂数据集的实验结果和分析表明，该模型不仅促进了微小车辆对象检测的准确性，而且还提高了车辆检测的准确性（精度达到94％）而不降低检测速度。

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