An Automatic System for the Real-Time Characterization of Vehicle Headlamp Beams Exploiting Image Analysis

被引:3
作者
Bevilacqua, Alessandro [1 ]
Gherardi, Alessandro [1 ]
Carozza, Ludovico [1 ]
机构
[1] Univ Bologna, Adv Res Ctr Elect Syst, I-41025 Bologna, Italy
关键词
Automatic system; automotive; beam characterization; eye-like segmentation; image analysis;
D O I
10.1109/TIM.2010.2045259
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
A vehicle's headlamp orientation and luminous and geometrical beam properties are a matter that is strictly ruled by the European Commission for transportation. To test the headlamps, a test system is usually manually aligned to the vehicle, and the human being has the definite opinion even on the beam-related measures. This paper presents a fully automatic system that exploits vision-based technology to extract the geometric parameters of the light profiles that are projected by vehicle headlamps in real time. The 3-D orientation of the longitudinal axis of the vehicle is recovered using stereoscopy. Furthermore, image analysis is used to automatically characterize the shadow-light border of a beam profile, as it would be perceived by an experienced human operator. A locally adaptive thresholding algorithm allows our system to automatically adjust to a wide range of light sources of different power. The alignment procedure and the beam characterization algorithm have been assessed through proper measuring apparatus that is capable of yielding accurate ground-truth data. In particular, the headlamp has been mounted on a special three-axis numerical control unit whose accuracy has been previously assessed, again using image analysis. Experimental results, which are carried out on a large number of different headlamps, show that our method is able to achieve accurate measurements in compliance with current regulations. Finally, it is worth remarking that our solution is fully automatic, and it just requires a simple setup procedure.
引用
收藏
页码:2630 / 2638
页数:9
相关论文
共 29 条
[11]   Determining the camera response from images: What is knowable? [J].
Grossberg, MD ;
Nayar, SK .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2003, 25 (11) :1455-1467
[12]  
Hoefflinger B, 2007, SPR SER ADV MICROELE, V26, P1, DOI 10.1007/978-3-540-44433-6
[13]  
KAZUHIRO F, 1995, Patent No. 5447363
[14]   THRESHOLDING FOR EDGE-DETECTION USING HUMAN PSYCHOVISUAL PHENOMENA [J].
KUNDU, MK ;
PAL, SK .
PATTERN RECOGNITION LETTERS, 1986, 4 (06) :433-441
[15]   Which pattern? Biasing aspects of planar calibration patterns and detection methods [J].
Mallon, John ;
Whelan, Paul F. .
PATTERN RECOGNITION LETTERS, 2007, 28 (08) :921-930
[16]   INVERSE PERSPECTIVE MAPPING SIMPLIFIES OPTICAL-FLOW COMPUTATION AND OBSTACLE DETECTION [J].
MALLOT, HA ;
BULTHOFF, HH ;
LITTLE, JJ ;
BOHRER, S .
BIOLOGICAL CYBERNETICS, 1991, 64 (03) :177-185
[17]  
MANN S, 1995, IS&T'S 48TH ANNUAL CONFERENCE - IMAGING ON THE INFORMATION SUPERHIGHWAY, FINAL PROGRAM AND PROCEEDINGS, P442
[18]  
Mitsunaga T, 1999, P 1999 IEEE COMP SOC, V1, P374
[19]  
Monti Angelo F, 2003, Med Dosim, V28, P91, DOI 10.1016/S0958-3947(02)00242-X
[20]  
MYSZKOWSKI K, 2008, SYNTHESIS LECT COMPU, P1