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

Cao, Jingwei; Song, Chuanxue; Song, Shenmin; Peng, Silun; Wang, Da; Shao, Yulong; Xiao, Feng

doi:10.3390/s20164646

Cited by 43 publications

(21 citation statements)

References 37 publications

(40 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SSD [14] detection algorithm combines the advantages of the Fast R-CNN series [15,16] algorithm and the YOLO algorithm and realizes the detection of targets of various sizes by generating candidate frames of different sizes on the multiscale feature inspection map. Such as literature [17,18] is used for vehicle target detection. Zheng and Chen [19] improved the cascading region of interest to increase the context information, which effectively improved the detection accuracy of small targets.…”

Section: Smart City Security Perception Related Workmentioning

confidence: 99%

A Vehicle Detection Model Based on 5G-V2X for Smart City Security Perception

Liu

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Security perception systems based on 5G-V2X have become an indispensable part of smart city construction. However, the detection speed of traditional deep learning models is slow, and the low-latency characteristics of 5G networks cannot be fully utilized. In order to improve the safety perception ability based on 5G-V2X, increase the detection speed in vehicle perception. A vehicle perception model is proposed. First, an adaptive feature extraction method is adopted to enhance the expression of small-scale features and improve the feature extraction ability of small-scale targets. Then, by improving the feature fusion method, the shallow information is fused layer by layer to solve the problem of feature loss. Finally, the attention enhancement method is introduced to increase the center point prediction ability and solve the problem of target occlusion. The experimental results show that the UA-DETRAC data set has a good detection effect. Compared with the vehicle detection capability before the improvement, the detection accuracy and speed have been greatly improved, which effectively improves the security perception capability based on the 5G-V2X system, thereby promoting the construction of smart cities.

show abstract

Section: Smart City Security Perception Related Workmentioning

confidence: 99%

A Vehicle Detection Model Based on 5G-V2X for Smart City Security Perception

Liu

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…As a result, great effort is being put towards incorporating sensors and microprocessors into various devices to create 'Smart Devices'. These devices size range from large (smart cars [1,2], smart homes [3][4][5]) to minuscule (smart watches [6,7] and other wearable devices [8][9][10]). Consequently, data are being generated and collected at an ever-growing rate, with projections of continual growth.…”

Section: Motivationmentioning

confidence: 99%

Colocalized Sensing and Intelligent Computing in Micro-Sensors

Hasan

Al-Ramini

Abdel‐Rahman

et al. 2020

Sensors

View full text Add to dashboard Cite

This work presents an approach to delay-based reservoir computing (RC) at the sensor level without input modulation. It employs a time-multiplexed bias to maintain transience while utilizing either an electrical signal or an environmental signal (such as acceleration) as an unmodulated input signal. The proposed approach enables RC carried out by sufficiently nonlinear sensory elements, as we demonstrate using a single electrostatically actuated microelectromechanical system (MEMS) device. The MEMS sensor can perform colocalized sensing and computing with fewer electronics than traditional RC elements at the RC input (such as analog-to-digital and digital-to-analog converters). The performance of the MEMS RC is evaluated experimentally using a simple classification task, in which the MEMS device differentiates between the profiles of two signal waveforms. The signal waveforms are chosen to be either electrical waveforms or acceleration waveforms. The classification accuracy of the presented MEMS RC scheme is found to be over 99%. Furthermore, the scheme is found to enable flexible virtual node probing rates, allowing for up to 4× slower probing rates, which relaxes the requirements on the system for reservoir signal sampling. Finally, our experiments show a noise-resistance capability for our MEMS RC scheme.

show abstract

“…This method relies upon the prior knowledge of vehicle objects. However, the method based on machine learning is not appropriate for detecting vehicles in different environments [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Robust Vehicle Detection Based on Improved You Look Only Once

Kumar¹,

Jailia²,

Varshney³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Vehicle detection is still challenging for intelligent transportation systems (ITS) to achieve satisfactory performance. The existing methods based on one stage and two-stage have intrinsic weakness in obtaining high vehicle detection performance. Due to advancements in detection technology, deep learning-based methods for vehicle detection have become more popular because of their higher detection accuracy and speed than the existing algorithms. This paper presents a robust vehicle detection technique based on Improved You Look Only Once (RVD-YOLOv5) to enhance vehicle detection accuracy. The proposed method works in three phases; in the first phase, the K-means algorithm performs data clustering on datasets to generate the classes of the objects. Subsequently, in the second phase, the YOLOv5 is applied to create the bounding box, and the Non-Maximum Suppression (NMS) technique is used to eliminate the overlapping of the bounding boxes of the vehicle. Then, the loss function CIoU is employed to obtain the accurate regression bounding box of the vehicle in the third phase. The simulation results show that the proposed method achieves better results when compared with other state-of-art techniques, namely Lightweight Dilated Convolutional Neural Network (LD-CNN), Single Shot Detector (SSD), YOLOv3 and YOLOv4 on the performance metric like precision, recall, mAP and F1-Score. The simulation and analysis are carried out on PASCAL VOC 2007, 2012 and MS COCO 2017 datasets to obtain better performance for vehicle detection. Finally, the RVD-YOLOv5 obtains the results with an mAP of 98.6% and Precision, Recall, and F1-Score are 98%, 96.2% and 97.09%, respectively.

show abstract

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

Cited by 43 publications

References 37 publications

A Vehicle Detection Model Based on 5G-V2X for Smart City Security Perception

A Vehicle Detection Model Based on 5G-V2X for Smart City Security Perception

Colocalized Sensing and Intelligent Computing in Micro-Sensors

Robust Vehicle Detection Based on Improved You Look Only Once

Contact Info

Product

Resources

About