SJTU数字图像处理课设-传统图像处理结合yolov5算法实现电车轨道ROI区域标注及障碍物检测项目源码+项目说明.7z

上传者: DeepLearning_ | 上传时间: 2022-12-13 16:26:15 | 文件大小: 910KB | 文件类型: 7Z
SJTU数字图像处理课设_传统图像处理结合yolov5算法实现电车轨道ROI区域标注及障碍物检测项目源码+项目说明.7z 【SJTU数字图像处理课程设计】 采用传统的数字图像处理方法(边缘检测,透射变换,霍夫变换等)对视频中的电车轨道进行检测和标注,并标注轨道所处的ROI区域,基于此ROI区域使用当下较为流行的YOLOv5目标检测深度学习算法进行区域内的障碍物识别与检测并将其标注。算法最终效果较好,可准确的检测两种环境(白天和夜晚)下的电车轨道并对轨道附近障碍物进行识别。算法识别效率为17FPS,效果较好。 主要任务为完成有轨电车轨道与轨道上障碍物的检测

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