基于暗通道去雾和深度学习的行人检测方法

A Pedestrian Detection Method Based on Dark Channel Defogging and Deep Learning

行人检测是实现智能交通与客流监控的关键技术,深度学习方法训练模型已经在行人检测领域取得了良好的效果。但是当训练样本质量不佳时,训练的模型往往不能得到令人满意的效果。为了提高雾霾天气与曝光较强环境下的行人检测效果,提出了将暗通道去雾算法应用于深度学习的样本预处理中,并使用快速深度卷积神经网络训练行人检测模型。在实验中,首先对10000张样本图片采用暗通道去雾算法进行预处理,之后分别使用有无暗通道去雾算法预处理的样本图片训练模型,最后比较这两种模型在不同场景下的模型检测准确率。实验结果表明,使用暗通道去雾预处理后的样本训练得到的深度模型具有更好的检测效果,在多种场景下的检测率都得到提升。

Pedestrian detection is the key technology to realize intelligent traffic and passenger flow monitoring. Currently, the training model of deep learning method has achieved good results in pedestrian detection. However, when the training samples are poor, the training model often fails to achieve good results. In order to improve the effect of pedestrian detection under hazy weather and strong exposure environment, the dark channel defogging algorithm is applied to pretreat deep learning samples. And pedestrian detection model is trained with fast deep convolutional neural network. In this experiment, the dark channel defogging algorithm is applied to preprocess the 10,000 sample images. After that, the sample images preprocessed by the defogging algorithm with and without dark channel are used to train model, respectively. Finally, detection accuracy of these two models under different scenarios are compared. The experimental results show that the depth model obtained by using the dark channel defogging pretreatment sample has a better detection effect and the detection rate increases under many scenarios.