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首页> 外文期刊>International Journal of Innovative Computing Information and Control >DETECTING AND RECOGNIZING TRAFFIC LIGHTS BY GENETIC APPROXIMATE ELLIPSE DETECTION AND SPATIAL TEXTURE LAYOUTS
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DETECTING AND RECOGNIZING TRAFFIC LIGHTS BY GENETIC APPROXIMATE ELLIPSE DETECTION AND SPATIAL TEXTURE LAYOUTS

机译：遗传近似椭圆检测和空间纹理布局对交通灯的检测与识别

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摘要

Traffic light detection is usually treated as a circle detection problem in computer vision. To handle more challenging cases in traffic light detection, this paper extends the circle detection problem to an approximate ellipse detection problem. For tackling the approximate ellipse detection problem, we propose a novel genetic approach which is more robust in handling appearance changes caused by perspective shape deformations and partial occlusions. To deal with the color recognition of each detected traffic light, we propose the design of a spatial texture layout feature which is more effective in handling illumination variations under different weather conditions and eliminating false alarms from irrelevant scene backgrounds. The experimental results show that the proposed method achieves an average recognition rate of 95.01%, with a false alarm rate below 2% based on 763 3-color traffic lights over 714 testing images, and demonstrates superior performance compared with an existing method.
机译：交通信号灯检测通常被视为计算机视觉中的圆圈检测问题。为了处理交通灯检测中更具挑战性的情况，本文将圆检测问题扩展到近似椭圆检测问题。为了解决近似椭圆检测问题，我们提出了一种新颖的遗传方法，该方法在处理由透视形状变形和部分遮挡引起的外观变化方面更强大。为了处理每个检测到的交通信号灯的颜色识别，我们提出了一种空间纹理布局功能的设计，该功能可以更有效地处理不同天气条件下的照明变化，并消除无关场景背景下的虚假警报。实验结果表明，所提出的方法基于714张测试图像上的763种三色交通信号灯，平均识别率达到95.01％，误报率低于2％，性能优于现有方法。

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