Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (1): 317-324.DOI: 10.3778/j.issn.1002-8331.2207-0166

• Engineering and Applications • Previous Articles    

RoadNetv2:Real-Time Algorithm for Highway Weak Abandoned Objects Detection

ZHU Xiaofeng, LI Lin, ZHANG Dejin, LUO Wenting   

  1. 1.College of Transportation and Civil Engineering, Fujian Agricultural and Forestry University, Fuzhou 350100, China
    2.College of Transportation Engineering, Nanjing Tech University, Nanjing 211816, China
    3.School of Architecture and Urban Planning, Shenzhen University, Shenzhen, Guangdong 518060, China
  • Online:2023-01-01 Published:2023-01-01



  1. 1.福建农林大学 交通与土木工程学院,福州 350100
    2.南京工业大学 交通运输工程学院,南京 211816
    3.深圳大学 建筑与城市规划学院,广东 深圳 518060

Abstract: Highway abandoned objects easily cause car out of control, resulting in traffic accidents. In order to solve the problems of low complexity of highway abandoned objects dataset and low detection accuracy and high FLOPS calculation of highway abandoned objects detection algorithm at present, a simulation scenario dataset expansion method and a RoadNetV2 highway abandoned objects detection algorithm are proposed. Simulation scenario dataset expansion method uses similar datasets to expand the scene simulation. RoadNetV2 highway abandoned objects detection algorithm adopts light-focus shallow information enhancement module and C3_CD feature extraction model as main components of Backbone, adopts CoordConv and custom Conv combination method to reduce the complexity of neck, and adopts multi-weight balance calculation strategy to assist Alpha-CIOU to weak objects efficient position regression. Experimental results show that compared with the current series of YOLO algorithms, the FLOPS calculation of RoadNetV2 highway abandoned objects detection algorithm is reduced by 14.54×109 at most to 12.4×109, and mAP(mean average precision) is improved by 3.5?percentage points to 61.1%, weight files are only 8.70 MB less by 4.98 MB. RoadNetV2 highway abandoned objects detection algorithm can meet the deployment requirements of embedded edge devices and mobile devices. Combined with self-developed inspection equipment, RoadNetV2 can solve the slack caused by manual inspection to a certain extent.

Key words: highway abandoned objects, weak objects, lightweight neural network, complex background, edge computing

摘要: 高速异物易引起汽车失控,造成交通安全事故。针对现阶段高速异物数据集复杂度低以及高速异物检测算法存在检测精度低、浮点计算量高等问题,提出了模拟场景数据集扩充方法和一种名为RoadNetV2的高速异物检测算法。模拟场景数据集扩充方法利用相似数据集进行场景模拟扩充。roadnetv2高速异物检测算法采用了light-focus浅层信息增强模块和C3_CD特征提取模型作为backbone主要组件,采用CoordConv与自定义Conv组合的方法降低neck部分的复杂度,采用多权值平衡计算策略辅助Alpha-CIOU进行弱小目标高效位置回归。实验证明RoadNetV2高速异物检测算法相比于现阶段同系列YOLO算法,浮点计算量最多降低了14.54×109仅为12.4×109,mAP最高提升了3.5个百分点达到61.1%,权重文件仅为8.70 MB减少了4.98 MB。RoadNetV2高速异物检测算法满足嵌入式边缘设备以及移动端设备的部署要求,搭配自主研发的巡检设备,可以一定程度上解决人工巡检所产生工作懈怠的情况。

关键词: 高速异物, 弱小目标, 轻量化神经网络, 复杂背景, 边缘计算