基于红外与可见光图像融合的交通标志检测

Traffic sign detection based on infrared and visible image fusion

针对大多数交通标志检测方法易受光照和天气影响的问题,提出一种基于红外与可见光融合的交通标志检测方法。首先,分别在红外与可见光图像中提取交通标志的形状和颜色特征,对目标进行粗定位,并将结果放入候选组内进行分类与整理;然后,将候选组放入卷积神经网络,对粗定位结果赋予相应的置信度;最后,根据置信度对红外与可见光的粗定位结果进行决策融合,并将融合结果展示在可见光图像中。实验结果表明,该算法对检测环境的变化更加鲁棒,在白天、夜晚和不良天气等多种环境下有效地提高了检测效率,具有良好的适用范围。

A traffic sign detection method based on infrared and visible image fusion is proposed to deal with the fact that the traffic sign detection is susceptible to light and weather.In the method,the shape and color features of traffic signs are extracted from infrared and visible images respectively to roughly locate the targets,and the results are put into the candidate groups for classification and sorting.Then the candidate groups are placed in the convolutional neural network to give corresponding confidence to the rough positioning results.Finally,the rough positioning results from infrared and visible light images are fused according to the confidence,and the fused results are displayed in visible images.The experimental results show that the proposed method is more robust to the change of detection environment,effectively improves the detection efficiency in various environments such as daytime,night and adverse weather conditions.Therefore,the application scope of the proposed method is wide.