基于卷积神经网络的列车检测算法研究

列车检测作为列车自动驾驶的核心技术,可以有效地降低列车追尾等事故造成的人身危险和财产损失。为实现精准的列车检测,选用改进的卷积神经网络(PVANET)对输入图像进行特征提取,在此基础上,采用候选区域网络,从生成的特征图里滑动搜索,判断出图像中可能为列车的区域位置,并进一步采用快速区域卷积神经网络对每个候选区域进行分类,计算出其所属类别的置信度,同时精确定位列车。经验证,该方法适应范围广、鲁棒性高,可以有效地检测不同环境光强及不同朝向的列车,保障列车安全,为列车自动驾驶及辅助驾驶提供安全保障。

Train Detection Algorithm Based on Convolutional Neural Network

As the key technique of the automatic driving, train detection can effectively reduce personal risks and the loss of property caused by train collision. To achieve precise train detection, the optimized convolutional neural network(PVANET) was adopted to extract the features from the input image. Then, the region proposal network was used for searching in the generated feature images, to determine the possible regional position of the train. The fast region convolutional neural network was adopted to classify each candidate region, calculate the confidence of its category, and accurately locate the train. The experimental results show that the proposed method with wide application range and high robustness, can effectively detect the light intensity and the trains in different directions, safeguard train safety, ensure the safety of trains and provide safety guarantees for automatic train driving and assisted driving.