摘要
为实现高速铁路周界侵限检测系统自动识别轨道区域的功能,提出了一种自适应的图像分割与识别算法。计算了每个场景的直线特征极大值以调节自适应参数,提出了新的基于边界点权重及区域面积的聚类组合规则,将碎片化区域快速组合成局部区域;简化了卷积神经网络,通过对卷积核进行预训练并在损失函数中增加稀疏项来提高特征图的差异性。在不使用显卡的前提下,对比实验结果表明所提算法的像素准确率最高(95.9%),计算时间最短(2.5 s),网络参数约为0.18×10^6个,在分割精准度、识别准确率、计算时间、人工操作复杂度和系统硬件成本等之间找到了有效平衡点,提高了铁路周界侵限检测系统的自动化程度和工作效率。
To recognize a monitored area automatically for a high-speed railway perimeter-intrusion detecting system, an adaptive image segmentation and recognition algorithm is proposed. The maximum linear feature of each scene is calculated to regulate the adaptive parameters. Moreover, a new combination rule based on the weight of the boundary point and the area size is proposed to rapidly combine the fragmented regions into local areas. A simplified convolutional neural network is designed, the convolutional kernels are pre-trained, and a sparse element is added into the loss function to enhance the diversity of the feature maps. Experimental comparison results indicate that without the graphics processing unit, the pixel accuracy of the proposed algorithm is highest(95.9%), the calculation time is the least(2.5 s), and the number of network parameters is about 0.18×10^6. The proposed algorithm considers an effective balance among the segmentation precision, recognition accuracy, calculation time, manual workload, and hardware cost of the system. Therefore, the automation and efficiency of the railway perimeter intrusion detection system are enhanced.
作者
王洋
朱力强
余祖俊
郭保青
Wang Yang;Zhu Liqiang;Yu Zujun;Guo Baoqing(School of Mechanical,Electronic and Control Engineering,Beijing Jiaotong University,Beijing 100044,China;Key Laboratory of Vehicle Advanced Manufacturing,Measuring and Control Technology,Beijing Jiaotong University,Beijing 100044,China)
出处
《光学学报》
EI
CAS
CSCD
北大核心
2019年第6期111-118,共8页
Acta Optica Sinica
基金
国家重点研发计划高速铁路系统安全保障课题(2016YFB1200401)
关键词
图像处理
场景分割
场景识别
多尺度边缘检测
卷积神经网络
image processing
image segmentation
image recognition
multi-scale edge detection
convolutional neural networks
