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Open AccessArticle

Front Vehicle Detection Algorithm for Smart Car Based on Improved SSD Model

State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China
Taizhou Automobile Power Transmission Research Institute, Jilin University, Taizhou 225322, China
Zhengzhou Yutong Bus Co., Ltd., Zhengzhou 450016, China
Author to whom correspondence should be addressed.
Sensors 2020, 20(16), 4646;
Received: 20 July 2020 / Revised: 14 August 2020 / Accepted: 17 August 2020 / Published: 18 August 2020
(This article belongs to the Section Intelligent Sensors)
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. View Full-Text
Keywords: computer vision; autonomous vehicle; SSD; deep learning; vehicle detection computer vision; autonomous vehicle; SSD; deep learning; vehicle detection
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Cao, J.; Song, C.; Song, S.; Peng, S.; Wang, D.; Shao, Y.; Xiao, F. Front Vehicle Detection Algorithm for Smart Car Based on Improved SSD Model. Sensors 2020, 20, 4646.

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