Available at: https://digitalcommons.calpoly.edu/theses/3098
Date of Award
6-2025
Degree Name
MS in Electrical Engineering
Department/Program
Electrical Engineering
College
College of Engineering
Advisor
Jane Zhang
Advisor Department
Electrical Engineering
Advisor College
College of Engineering
Abstract
Traffic accidents pose a significant threat to public safety, causing millions of deaths and injuries worldwide each year. While efforts to reduce accidents have seen limited progress in recent years, improving emergency response times through automated detection systems is a promising avenue for saving lives. This thesis describes the development of machine learning-based traffic accident detection systems, exploring both video classification and image detection models. The models are trained on a new dataset deemed the Cal Poly Traffic Accident Dataset, an extension of the existing Car Accident Detection and Prediction (CADP) dataset with a precise collision annotations. Two systems were developed and evaluated. The first is centered around the R(2+1)D video classification model, in which the system processes temporal information in isolated regions of videos. And the second, is the YOLOv8-nano image detection model, which detects accidents on a frame-by-frame basis without temporal context. Experimental results revealed that despite lacking temporal awareness, the YOLO-based model outperformed the system with the R(2+1)D model. This suggests that ensuring strong spatial feature extraction is crucial before incorporating temporal processing in crash recognition systems.