摘要
针对深度学习方法在视觉车辆检测过程中对小目标车辆漏检率高和难以实现嵌入式实时检测的问题,该文基于Tiny YOLOV3算法提出了增强Tiny YOLOV3模型,并通过匈牙利匹配和卡尔曼滤波算法实现目标车辆的跟踪。在车载Jetson TX2嵌入式平台上,分别在白天和夜间驾驶环境下进行了对比试验。试验结果表明:与Tiny YOLOV3模型相比,增强Tiny YOLOV3模型的车辆检测平均准确率提高4.6%,平均误检率减少0.5%,平均漏检率降低7.4%,算法平均耗时增加43.8 ms/帧;加入跟踪算法后,本文算法模型的车辆检测平均准确率提高10.6%,平均误检率减少1.2%,平均漏检率降低23.6%,平均运算速度提高5倍左右,可达30帧/s。结果表明,所提出的算法能够实时准确检测出目标车辆,为卷积神经网络模型的嵌入式工程应用提供了参考。
For intelligent vehicles and advanced driving assistant systems, real-time and accurate vehicle objects detection and tracking through on-board visual sensors are conducive to discovering potential dangers, and can take timely warning to drivers or measures to control vehicle braking and steering systems to avoid traffic accidents by active safety system. In recent years, vehicle detection based on deep learning has become a research hotspot. Although the deep learning method has made a significant breakthrough in vehicle detection precision, it will lead to high missed detection rate of small vehicle targets and rely on expensive computing resources in visual vehicle detection tasks, which is difficult to achieve in embedded real-time applications. Further analysis shows that the main reason for the above problems is that deep convolution neural network cannot reasonably prune network layer parameters, especially cannot reasonably utilize the shallow semantic information. On the contrary, a series of operations at the lower sampling layers will lead to the loss of vehicle information, especially for the small vehicle objects. Therefore, how to effectively extract and utilize the semantic information of small vehicle objects is a problem to be solved in this paper. On this basis, the problem of pruning network layer parameters reasonably was discussed.For the detection algorithm, on the one hand, based on visual analysis of receptive field of Tiny YOLOV3 network shallow layers, the use of shallow semantic information was enhanced by constructing a shallow feature pyramid structure, on the other hand, the shallow down sampling layer was replaced by convolution layer to reduce the semantic information loss of shallow network layers and increase the shallow layer features of vehicle objects to be extracted. Combine the above 2 aspects, the enhanced Tiny YOLOV3 network was proposed. For the tracking algorithm, because of the high frame rate of the vehicle-mounted camera, assuming that the vehicle objects in the adjac
作者
刘军
后士浩
张凯
张睿
胡超超
Liu Jun;Hou Shihao;Zhang Kai;Zhang Rui;Hu Chaochao(School of Automotive and Traffic Engineering,Jiangsu University, Zhenjiang 212013,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2019年第8期118-125,共8页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自然科学基金项目(51275212)
