基于无人机和深度学习技术的车辆事故碎片目标检测

Homayra Alam, Damian Valles
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引用次数: 2

摘要

通过利用无人机、深度学习和目标检测来优化操作并重新开放道路,可以改善道路碎片清理过程。常见的碎片是车辆事故后从车辆中飞出的未固定的物品。事故后道路碎片的清理程序繁琐而敏感。它需要大量的劳动力和一个耗时的过程来正确地搬运碎片。本文旨在使用无人机检测道路上的碎片,以最大限度地减少清洁过程的时间。目标检测API采用SSD预训练模型和Faster R-CNN进行目标检测。准确率图、评价矩阵和检测盒分数决定了碎片检测的有效模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Debris Object Detection Caused by Vehicle Accidents Using UAV and Deep Learning Techniques
The road debris clean-up process can be improved by utilizing drones, Deep Learning, and object detection to optimize the operation and re-open roads for traffic. Common debris is unsecured items that fly out from vehicles after a vehicle accident. The cleaning procedure of the road debris after an accident is cumbersome and sensitive. It demands much workforce and a time-consuming process to haul debris properly. The paper aims to detect debris on the road using a drone to minimize the time cleaning procedure. Object detection API with the pre-trained model of SSD and Faster R-CNN is used for object detection. The accuracy graphs, evaluation matrix, and detection box score determine the efficient model for debris detection.
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