A symmetry-based forward vehicle detection and collision warning system on Android smartphone

Jheng, Yu-Jie; Yen, Yu-Hua; Sun, Tsung-Ying

doi:10.1109/icce-tw.2015.7216861

Cited by 6 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The detection accuracy was about 94.6% with a running time of 80 ms/fps. In [ 68 ], the symmetry measurement function was also based on the contour feature to extract the symmetry axis and the center point, compared with [ 67 ], the author transplanted the program package to the Android system, and the detection accuracy was about 92.38%, with a running time of 33 ms/fps. Limited by the computing performance of the Android system, although this algorithm meets the requirements of good detection accuracy and real-time performance, it can only be used for the detection of simple scenes, and the results did not have a strong reference.…”

Section: Vehicle Detection: Vision-based Methodsmentioning

confidence: 99%

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Liu

Yuan

et al. 2021

Sensors

View full text Add to dashboard Cite

Unmanned ground vehicles (UGVs) have great potential in the application of both civilian and military fields, and have become the focus of research in many countries. Environmental perception technology is the foundation of UGVs, which is of great significance to achieve a safer and more efficient performance. This article firstly introduces commonly used sensors for vehicle detection, lists their application scenarios and compares the strengths and weakness of different sensors. Secondly, related works about one of the most important aspects of environmental perception technology—vehicle detection—are reviewed and compared in detail in terms of different sensors. Thirdly, several simulation platforms related to UGVs are presented for facilitating simulation testing of vehicle detection algorithms. In addition, some datasets about UGVs are summarized to achieve the verification of vehicle detection algorithms in practical application. Finally, promising research topics in the future study of vehicle detection technology for UGVs are discussed in detail.

show abstract

Section: Vehicle Detection: Vision-based Methodsmentioning

confidence: 99%

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Liu

Yuan

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Vehicle detection is a key area of research in computer vision and has numerous potential applications in the areas of intelligent transportation, driver monitoring technologies, and autonomous driving [1][2] . Traditional target detection algorithms [3][4][5] and deep learning-based target detection algorithms [6] are two commonly used methods for vehicle detection. Deep learning-based target detection algorithms have better detection results than the traditional target detection algorithms.…”

Section: Introductionmentioning

confidence: 99%

A lightweight vehicles detection network model based on YOLOv5

Xu,

Liu,

Liu

et al. 2023

Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023)

View full text Add to dashboard Cite

For the existing target detection algorithm with large number of parameters and computation, which cannot be broadly applicable in the vehicle detection field, a lightweight vehicle detection network model is designed in this paper. The vehicle detection model mainly uses RFB-PANet to replace the PANet feature fusion network of YOLOv5s to obtain rich feature information to make the model more accurate, and uses GhostConv structure to optimize the traditional convolutional layer of YOLOv5s to have less parameters and computation of the model, so as to achieve the purpose of lightweights vehicle detection model. The experimental results show that proposed vehicle detection network model reduces the number of parameters by 25.0% and the computational effort by 18.75% compared with the original YOLOV5 network model. The mAp is increased by 2.1% and 1.2% on Pascal VOC dataset and vehicle dataset, respectively, thus achieving a good balance of detection accuracy and light weight

show abstract