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Research on Small Target Detection in Driving Scenarios Based on Improved Yolo Network

机译：基于改进的YOLO网络的驾驶场景小目标检测研究

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摘要

The obtainment of road condition information during driving is extremely important for a driver. However, drivers usually cannot notice multiple information at the same time, which definitely increases certain safety risks. Considering this problem, this paper designs a road information collection plus alarm system based on artificial intelligence to monitor road information. The underlying core algorithm of this system adopts the YOLO v3 network with the best comprehensive detection performance in the end-to-end network. We use this network's advantage of fast detection speed to optimize on its original basis, and propose to "copy'' part of the backbone network to build an auxiliary network, which enhances its feature extraction capability. Further, we apply the attention mechanism to the feature information fusion of the auxiliary network and the backbone network, suppress the invalid information channel, and improve the network processing efficiency. Besides, the training part of the network is optimized, and the mAP (mean Average Precision) is improved by setting the scale that meets the target to be detected. Through the test, the average test accuracy of the optimized network model reaches 84.76%, and the real-time detection speed on the 2080Ti reaches 41FPS. Compared with the previous network, the detection accuracy increases by 5.43% after optimization.
机译：在驾驶期间获得道路状况信息对于驾驶员来说非常重要。但是，司机通常不能同时注意到多个信息，这绝对增加了某些安全风险。考虑到这个问题，本文设计了一种基于人工智能的道路信息收集加报警系统，以监控道路信息。该系统的底层核心算法采用Yolo V3网络，在端到端网络中具有最佳的综合检测性能。我们使用此网络的优势快速检测速度来优化原始基础，并建议“复制”骨干网络的一部分来构建辅助网络，这提高了其特征的提取能力。此外，我们将注意力机制应用于其功能信息融合的辅助网络和骨干网，抑制无效的信息通道，提高网络处理效率。此外，网络的训练部分通过设置规模来改进地图（平均平均精度）符合要检测的目标。通过测试，优化的网络模型的平均测试精度达到84.76％，而2080Ti上的实时检测速度达到41fps。与以前的网络相比，检测精度增加了5.43优化后％。

著录项

  • 来源
    《Quality Control, Transactions》 |2020年第2020期|27574-27583|共10页
  • 作者

    Xu Qiwei; Lin Runzi; Yue Han; Huang Hong; Yang Yun; Yao Zhigang;

  • 作者单位

    Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400044 Peoples R China;

    Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400044 Peoples R China;

    State Grid Corp China Northeast Branch Shenyang 110180 Peoples R China;

    Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400044 Peoples R China;

    Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400044 Peoples R China;

    Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400044 Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Convolutional neural network; residual network; target detection; YOLO v3;

    机译：卷积神经网络;剩余网络;目标检测;YOLO V3;

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