基于类间距优化的分心驾驶行为识别模型训练方法

A Metric Space Optimized Method for Driver Distraction Recognition Model Training

分心驾驶行为识别任务可以看作细粒度图像分类任务,即图像中较小区域所包含的特征决定了该图像的类别,如一张图像是正常驾驶还是与副驾驶聊天完全由驾驶员的脸部朝向来决定。对于那些图像差异很小的类别,图像分类通常训练方法训练出的模型无法高精度地区分。针对这一问题,提出了基于类间距优化的分心驾驶行为识别模型训练方法,通过增大模型从异类图像所提取特征向量之间的欧式距离,使得模型学到可以区分那些图像差异很小的类别的细微特征,进而提高模型对这些类别的分类准确率。该方法实现了端到端的模型训练,既不增加模型的推理时延,又不引入额外监督信息。State Farm数据集上的试验表明,与图像分类通常训练方法比,该训练方法有效提高了模型的准确率。

Driver distraction recognition task can be regarded as a fine-grained image classification task,i.e.,the features contained in a small area of the image determine the category of it.For example,whether a driver is driving normally or chatting with the co-pilot is only determined by the driver’s face orientation.For those categories with slight image differences,the model trained by ordinary image classification method is usually unable to distinguish them with high precision.To solve this problem,a metric space optimized method of distracted driving behavior recognition model training is proposed.By increasing the Euclidean distance between the feature vectors extracted from images of different categories,the model can learn the subtle features to classify these categories,and then improve the model's classification accuracy.The method realizes end-to-end model training without increasing the inference time or introducing in additional supervision information.Experiments on the State Farm dataset show that compared with the ordinary training methods of image classification,the proposed method effectively improves the accuracy of the model.