Front Vehicle Detection Algorithm for Smart Car Based on Improved SSD Model
Front Vehicle Detection Algorithm for Smart Car Based on Improved SSD Model
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DOI：
10.3390/s20164646
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发表时间：
2020-08-01
期刊：
影响因子：
3.9
通讯作者：
Xiao, Feng
中科院分区：
文献类型：
--
作者：
Cao, Jingwei;Song, Chuanxue;Xiao, Feng
车辆检测是智能汽车环境感知技术中不可或缺的一部分。针对传统车辆检测易受环境条件限制，且不具备准确性和实时性的问题，提出一种基于改进SSD模型的智能车前方车辆检测算法。单镜头多盒检测器（SSD）是当前主流的基于深度学习的目标检测框架之一。本文首先简要介绍了SSD网络模型，分析和总结了其在车辆检测中存在的问题和不足。然后，对SSD网络模型进行了有针对性的改进，包括对SSD模型的基本结构进行了重大改进，在网络训练中使用了加权掩码，并增强了损失函数。最后，基于KITTI视觉基准套件和自制的车辆数据集进行车辆检测实验，观察算法在不同复杂环境和天气条件下的检测性能。基于KITTI数据集的测试结果表明，mAP值达到92.18%，平均每帧处理时间为15 ms。与现有的基于深度学习的检测方法相比，该算法可以同时获得准确性和实时性。同时，该算法对复杂的交通环境具有良好的鲁棒性和环境适应性，对恶劣的天气条件具有较强的抗干扰能力。这些因素对于确保智能汽车在真实的交通场景中准确、高效地运行具有重要意义，有利于极大地降低交通事故的发生率，充分保护人民群众的生命财产安全。
Vehicle detection is an indispensable part of environmental perception technology for smart cars. Aiming at the issues that conventional vehicle detection can be easily restricted by environmental conditions and cannot have accuracy and real-time performance, this article proposes a front vehicle detection algorithm for smart car based on improved SSD model. Single shot multibox detector (SSD) is one of the current mainstream object detection frameworks based on deep learning. This work first briefly introduces the SSD network model and analyzes and summarizes its problems and shortcomings in vehicle detection. Then, targeted improvements are performed to the SSD network model, including major advancements to the basic structure of the SSD model, the use of weighted mask in network training, and enhancement to the loss function. Finally, vehicle detection experiments are carried out on the basis of the KITTI vision benchmark suite and self-made vehicle dataset to observe the algorithm performance in different complicated environments and weather conditions. The test results based on the KITTI dataset show that the mAP value reaches 92.18%, and the average processing time per frame is 15 ms. Compared with the existing deep learning-based detection methods, the proposed algorithm can obtain accuracy and real-time performance simultaneously. Meanwhile, the algorithm has excellent robustness and environmental adaptability for complicated traffic environments and anti-jamming capabilities for bad weather conditions. These factors are of great significance to ensure the accurate and efficient operation of smart cars in real traffic scenarios and are beneficial to vastly reduce the incidence of traffic accidents and fully protect people's lives and property.