论文标题
使用街道场景图像和深度学习来绘制郊区自行车道
Mapping suburban bicycle lanes using street scene images and deep learning
论文作者
论文摘要
道路自行车道提高了骑自行车者的安全性，并鼓励参与骑自行车进行积极的运输和娱乐。由于许多地方当局负责部分基础设施，因此自行车道的官方地图和数据集可能是过时的和不完整的。即使是“众包”的数据库也可能存在巨大的差距，尤其是在受欢迎的大都市地区之外。本文提出了一种通过从每条道路上拍摄样本街景图像，然后应用经过训练以识别自行车车道符号的深度学习模型，从而在调查区域中创建自行车道图的方法。然后将检测到自行车道标记的坐标列表与有关道路网络的地理空间数据相关联，以记录自行车道路线。该方法用于成功地为墨尔本外郊区的调查区域建造地图。它能够识别以前未记录在州政府官方数据集，OpenStreetMap或Google Maps的“骑自行车”层的自行车道。
On-road bicycle lanes improve safety for cyclists, and encourage participation in cycling for active transport and recreation. With many local authorities responsible for portions of the infrastructure, official maps and datasets of bicycle lanes may be out-of-date and incomplete. Even "crowdsourced" databases may have significant gaps, especially outside popular metropolitan areas. This thesis presents a method to create a map of bicycle lanes in a survey area by taking sample street scene images from each road, and then applying a deep learning model that has been trained to recognise bicycle lane symbols. The list of coordinates where bicycle lane markings are detected is then correlated to geospatial data about the road network to record bicycle lane routes. The method was applied to successfully build a map for a survey area in the outer suburbs of Melbourne. It was able to identify bicycle lanes not previously recorded in the official state government dataset, OpenStreetMap, or the "biking" layer of Google Maps.
