Vehicle Lighting Recognition System Based on Erosion Algorithm and Effective Area Separation in 5G Vehicular Communication Networks

Abstract

For the safety of meeting vehicles at night, a design scheme of vehicle lighting recognition system for 5G vehicular communication networks is proposed in this paper. Firstly, a camera is used for image acquisition, and then the acquired RGB image is converted to a hue-saturation-lightness (HSL) image in Dedicated cloud. Secondly, a 3×3 convolution kemel is constructed, and the threshold of this HSL is used to create a mask. In which we traverse the original image data and perform uitwise operations on the original image, as well as mask by using the Urightest point as the center of calculation. Lastly, the effective area of the image is divided according to the farthest point of the lane line extension, which can shield the sky and street lights. In addition, all of interference points are excluded according to the symmetry of the headlights shape, the number of false detection is reduced effectively. So, the highlight areas can be marked to indicate the lights from the front vehicles. The results of the analysis are issued to the driving assistant system. The experimental results show that the system can identify the front vehicle lighting with an accuracy of 96.8%. With the help of the identified lighting position, the matrix lamp is set to realize the dynamic management of the beam, and it can be applied to traffic at night for preventing traffic accidents.

Editorial: Modeling and Applications of Optoelectronic Devices for Access Networks

“…Applications of optoelectronic devices can provide good prospects for the development of access networks [9,10]. A photonic-assisted channelized receiver, which is based on the spectrum analysis, was developed for multi-band microwave signals (Huang et al).…”

A Pointer Type Instrument Intelligent Reading System Design Based on Convolutional Neural Networks

“…In order to solve this problem, many automatic recognition numerical reading algorithms based on computer readings have emerged in recent years. The existing pointer instrument recognition algorithms can be divided into two kinds, traditional algorithms based on digital image processing technology and modern algorithms based on machine learning or deep learning [2]. Traditional algorithms are the foundation of dilation and corrosion, and noise reduction filtering and feature matching of image matrixes must be done to perform target recognition.…”

Dynamic Adjustment and Distinguishing Method for Vehicle Headlight Based on Data Access of a Thermal Camera

“…Currently, vehicle detection is mostly based on visual images [3][4][5][6][7][8][9][10]. The visual image is not clear at night, and the detail of the vehicle is also unclear.…”

“…The visual image is not clear at night, and the detail of the vehicle is also unclear. In order to overcome the problem, a number of papers have been published to detect vehicles at night by identifying the shape and track of the headlights [3][4][5][6][7][8][9][10]. Many studies were both detecting vehicles via headlights pairing and trajectories matching [3,4].…”

“…Given that the headlights were usually white in color, the inputted images were usually transformed into different color spaces. The dominant color components in Red Green Blue (RGB) images were then processed by the threshold to extract blobs for the headlight [7]. However, this nighttime vehicle detection method depended on clear headlights or taillights shape [5,[8][9][10], and the existence of high beams glares was ignored.…”