论文标题
自主dozer的对象检测
Object Detection for Autonomous Dozers
论文作者
论文摘要
我们介绍了一种新型的自动驾驶汽车 - 一种自主推土机，有望以有效，健壮和安全的方式完成建筑工地任务。为了更好地处理推土机的路径规划并确保建筑工地的安全性，对象检测是感知任务中最关键的组成部分之一。在这项工作中，我们首先通过开车来收集建筑工地数据。然后，我们彻底分析数据以了解其分布。最后，对两个众所周知的对象检测模型进行了训练，他们的性能通过广泛的训练策略和超参数进行了基准测试。
We introduce a new type of autonomous vehicle - an autonomous dozer that is expected to complete construction site tasks in an efficient, robust, and safe manner. To better handle the path planning for the dozer and ensure construction site safety, object detection plays one of the most critical components among perception tasks. In this work, we first collect the construction site data by driving around our dozers. Then we analyze the data thoroughly to understand its distribution. Finally, two well-known object detection models are trained, and their performances are benchmarked with a wide range of training strategies and hyperparameters.
