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Open AccessArticle

Pedestrian Detection Algorithm for Intelligent Vehicles in Complex Scenarios

1
State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
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Taizhou Automobile Power Transmission Research Institute, Jilin University, Taizhou 225322, China
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School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China
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Zhengzhou Yutong Bus Co., Ltd., Zhengzhou 450016, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(13), 3646; https://doi.org/10.3390/s20133646
Received: 25 May 2020 / Revised: 25 June 2020 / Accepted: 27 June 2020 / Published: 29 June 2020
(This article belongs to the Section Intelligent Sensors)
Pedestrian detection is an important aspect of the development of intelligent vehicles. To address problems in which traditional pedestrian detection is susceptible to environmental factors and are unable to meet the requirements of accuracy in real time, this study proposes a pedestrian detection algorithm for intelligent vehicles in complex scenarios. YOLOv3 is one of the deep learning-based object detection algorithms with good performance at present. In this article, the basic principle of YOLOv3 is elaborated and analyzed firstly to determine its limitations in pedestrian detection. Then, on the basis of the original YOLOv3 network model, many improvements are made, including modifying grid cell size, adopting improved k-means clustering algorithm, improving multi-scale bounding box prediction based on receptive field, and using Soft-NMS algorithm. Finally, based on INRIA person and PASCAL VOC 2012 datasets, pedestrian detection experiments are conducted to test the performance of the algorithm in various complex scenarios. The experimental results show that the mean Average Precision (mAP) value reaches 90.42%, and the average processing time of each frame is 9.6 ms. Compared with other detection algorithms, the proposed algorithm exhibits accuracy and real-time performance together, good robustness and anti-interference ability in complex scenarios, strong generalization ability, high network stability, and detection accuracy and detection speed have been markedly improved. Such improvements are significant in protecting the road safety of pedestrians and reducing traffic accidents, and are conducive to ensuring the steady development of the technological level of intelligent vehicle driving assistance. View Full-Text
Keywords: driving assistance; intelligent vehicle; YOLOv3; convolutional neural network; pedestrian detection driving assistance; intelligent vehicle; YOLOv3; convolutional neural network; pedestrian detection
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MDPI and ACS Style

Cao, J.; Song, C.; Peng, S.; Song, S.; Zhang, X.; Shao, Y.; Xiao, F. Pedestrian Detection Algorithm for Intelligent Vehicles in Complex Scenarios. Sensors 2020, 20, 3646.

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