The dynamic and unpredictable nature of road traffic necessitates effective
accident detection methods for enhancing safety and streamlining traffic
management in smart cities. This paper offers a comprehensive exploration study
of prevailing accident detection techniques, shedding light on the nuances of
other state-of-the-art methodologies while providing a detailed overview of
distinct traffic accident types like rear-end collisions, T-bone collisions,
and frontal impact accidents. Our novel approach introduces the I3D-CONVLSTM2D
model architecture, a lightweight solution tailored explicitly for accident
detection in smart city traffic surveillance systems by integrating RGB frames
with optical flow information. Our experimental study's empirical analysis
underscores our approach's efficacy, with the I3D-CONVLSTM2D RGB + Optical-Flow
(Trainable) model outperforming its counterparts, achieving an impressive 87\%
Mean Average Precision (MAP). Our findings further elaborate on the challenges
posed by data imbalances, particularly when working with a limited number of
datasets, road structures, and traffic scenarios. Ultimately, our research
illuminates the path towards a sophisticated vision-based accident detection
system primed for real-time integration into edge IoT devices within smart
urban infrastructures.

本研究对现有的事故检测技术进行了全面的探索研究，重点介绍了尾部碰撞、侧面碰撞和正面碰撞等不同类型的交通事故，并引入了适用于智慧城市交通监控系统的 I3D-CONVLSTM2D 模型架构，结合 RGB 帧和光流信息进行事故检测。实验分析验证了该方法的有效性，该模型在平均精度（Mean Average Precision，MAP）方面表现优异，达到了 87%。同时，本研究还详细阐述了数据不平衡在有限数据集、道路结构和交通场景方面带来的挑战，最终为基于视觉的事故检测系统在智慧城市基础设施中实时集成到边缘物联网设备提供了路径指引。