Pedestrian Detection Algorithm for Intelligent Vehicles in Complex Scenarios
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 a...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/13/3646 |
| _version_ | 1797563785179824128 |
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| author | Jingwei Cao Chuanxue Song Silun Peng Shixin Song Xu Zhang Yulong Shao Feng Xiao |
| author_facet | Jingwei Cao Chuanxue Song Silun Peng Shixin Song Xu Zhang Yulong Shao Feng Xiao |
| author_sort | Jingwei Cao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T18:48:13Z |
| format | Article |
| id | doaj.art-3702173e2d46482ea71683d8ec38b846 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T18:48:13Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3702173e2d46482ea71683d8ec38b8462023-11-20T05:19:23ZengMDPI AGSensors1424-82202020-06-012013364610.3390/s20133646Pedestrian Detection Algorithm for Intelligent Vehicles in Complex ScenariosJingwei Cao0Chuanxue Song1Silun Peng2Shixin Song3Xu Zhang4Yulong Shao5Feng Xiao6State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, ChinaState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, ChinaState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, ChinaSchool of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, ChinaState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, ChinaZhengzhou Yutong Bus Co., Ltd., Zhengzhou 450016, ChinaState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, ChinaPedestrian 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.https://www.mdpi.com/1424-8220/20/13/3646driving assistanceintelligent vehicleYOLOv3convolutional neural networkpedestrian detection |
| spellingShingle | Jingwei Cao Chuanxue Song Silun Peng Shixin Song Xu Zhang Yulong Shao Feng Xiao Pedestrian Detection Algorithm for Intelligent Vehicles in Complex Scenarios Sensors driving assistance intelligent vehicle YOLOv3 convolutional neural network pedestrian detection |
| title | Pedestrian Detection Algorithm for Intelligent Vehicles in Complex Scenarios |
| title_full | Pedestrian Detection Algorithm for Intelligent Vehicles in Complex Scenarios |
| title_fullStr | Pedestrian Detection Algorithm for Intelligent Vehicles in Complex Scenarios |
| title_full_unstemmed | Pedestrian Detection Algorithm for Intelligent Vehicles in Complex Scenarios |
| title_short | Pedestrian Detection Algorithm for Intelligent Vehicles in Complex Scenarios |
| title_sort | pedestrian detection algorithm for intelligent vehicles in complex scenarios |
| topic | driving assistance intelligent vehicle YOLOv3 convolutional neural network pedestrian detection |
| url | https://www.mdpi.com/1424-8220/20/13/3646 |
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