基于改进深度卷积网络的铁路入侵行人分类算法

异物侵入铁路限界严重影响行车安全,识别铁路限界内的人员侵入对保证铁路运营安全具有重要意义。由于既有铁路图像异物侵入检测系统只能检测报警图像,无法区分是人员侵入的正确报警还是光线干扰导致的误报警,为了降低上述误报警,本文建立了铁路异物侵限报警样本的训练集和测试集,提出了将改进的深度卷积网络提取的高层Alex特征和HOG特征相结合并用于深度卷积网络模型训练的分类算法。首先引入了改进的AlexNet深度卷积神经网络模型,提取了自动学习的Alex高层特征,然后将其与HOG特征相结合形成Alex-HOG组合特征,最后利用组合特征对分类网络进行训练。铁路异物侵限报警测试样本库的实验表明,该方法对1 498张测试样本图像的识别准确率高达98.46%,时间为3.78s,实时性和准确率均有较大提高,对降低系统误报率具有重大意义。

Pedestrian intruding railway clearance classification algorithm based on improved deep convolutional network

Objects intruding railway clearance pose great threat to normal railway operations. Identifying intruding pedestrians within the railway clearance limit was of great significance to ensure the safety of railway operations. The existing railway intrusion detection system only detected the intrusion, but did not distinguish whether it was a true alarm of pedestrian intrusion or false alarm caused by light interferences. To reduce false alarms, a training and test set of the alarm image samples were established. A pedestrian classification algorithm based on improved deep convolutional network, trained with combined features of HOG and high-level Alex was then proposed. First, an improved AlexNet deep convolutional neural network model was introduced to extract high-level Alex features by automatic learning; the extracted features were then combined with HOG features to form the combined features of Alex-HOG. Finally, the combined features were used to train the classification network. Experiments on the test set show that the proposed method has a high recognition accuracy of 98.46% in 3.78 s for 1 498 test image samples. The improvements in both accuracy and real-time performance will greatly reduce the false alarm rate of the railway intrusion detection system.