计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 290-294.doi: 10.11896/jsjkx.201200113
刘林芽, 吴送英, 左志远, 曹子文
LIU Lin-ya, WU Song-ying, ZUO Zhi-yuan, CAO Zi-wen
摘要： 铁路沿线地段边坡落石检测对保障铁路沿线通车安全具有重要的现实意义。现有的检测方法存在检测成本高、操作复杂等缺点,针对以上问题,文中提出使用智能手机及民用相机结合补光器在实地多山地区采集多尺寸、多形状的各类岩石样本,利用深度卷积网络进行学习,提取落石样本相应特征进行训练,引入YOLOv3算法,构建山区铁路边坡落石检测深度学习模型,从而实现对山区铁路沿线地段边坡落石的实时检测,此外设置Faster RCNN算法作为平行对比实验。实验结果表明,两种检测算法都能达到较高的检测精度,YOLOv3算法较Faster RCNN算法的检测精度相对偏低,但其对体积较小的落石目标更加敏感,更具捕捉性,且检测速度更快,更能满足实际工程的需要。
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