Accident Debris Detection with UAV Using Deep Learning and Estimate Debris Perimeter




Alam, Homayra

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Road debris clean-up process can be improved by the utilization of drones, Deep Learning, and detection to optimize the operation and re-open roads for traffic. Common debris is unsecured items that fly out from vehicles after an 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 project aims to detect debris on the road using a drone to minimize the time cleaning procedure by generating a perimeter in correlation with GPS information. This thesis provides a framework for development of a Deep Learning model with Computer Vision using feature extraction and object recognition to detect debris and calculate the perimeter area. Currently, clean-up crews perform their task by observation to identify debris objects in different weather conditions. Debris can be comprised of dangerous chemicals, vehicle fluids, pesticides that may cause specialized crews for proper removal and delay the re-opening of roads. In contrast, the drone can fly faster and detect desired objects using the DL model more precisely. The perimeter information from the drone’s data will also optimize the cleaning procedure, improve time, and clean efficiency.



machine learning, deep learning, debris, object detection, tensorflow


Alam, H. (2021). Accident debris detection with UAV using deep learning and estimate debris perimeter (Unpublished thesis). Texas State University, San Marcos, Texas.


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