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license: apache-2.0
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# Traffic Accident Detection
## Overview
The [DETR](https://huggingface.co/facebook/detr-resnet-50) (DEtection Transfomer) model utilized in this implementation serves as a sophisticated solution for accident detection. This state-of-the-art model leverages the power of transformers, originally designed for natural language processing, to excel in object detection tasks. Trained on a diverse dataset, the DETR model demonstrates its capability to identify and locate objects within images, particularly excelling in the crucial task of accident detection within traffic scenes.
Harnessing advanced computer vision techniques, DETR offers unparalleled accuracy and efficiency in recognizing potential incidents, providing valuable insights for enhancing road safety. Its utilization is pivotal in real-time monitoring and analysis, empowering applications geared towards automated accident detection and response systems.
This DETR model is equipped with a robust post-processing pipeline, incorporating Non-Maximum Suppression (NMS) to refine detections and deliver precise and actionable results. Combined with efficient inference times, this DETR model stands as a powerful tool in the realm of accident detection, contributing to the development of intelligent and safety-focused systems in various domains.
## Dataset
Introducing a cutting-edge approach to accident detection, this model employs the DETR (DEtection Transfomer) architecture, specifically designed to seamlessly identify accidents within a comprehensive scene captured in a single image. Unlike traditional methods, this innovative model operates within the context of full images, leveraging the power of transformer-based object detection.
Table 1: When we use dataset focuses on accident label, model fails to detect accidents when traffic jams.
| traffic jams | traffic jams |
|-------|-------|
|  |  |
Table 2: When we use multi label dataset (accident and vehicle), model can detect accidents accurately without reducing detection performance when traffic jams
| traffic jams | traffic jams | accident | accident |
|-------|-------|------|-------|
|  |  |  |  |
Trained on a diverse and multilabel dataset, including 'accident' and 'vehicle' labels, the model excels in simultaneously recognizing both accident-related incidents and the presence of vehicles. This dual-label dataset enhances the model's capacity to comprehensively understand and interpret complex traffic scenarios, making it a potent tool for real-time accident detection and analysis.
By adopting a holistic perspective on the entire image, this DETR-based model contributes to a more robust and nuanced understanding of potential accidents, fostering advancements in automated safety systems. Its proficiency in detecting accidents within the broader context of traffic scenes positions it as a valuable asset for applications dedicated to enhancing road safety and emergency response.
[](https://universe.roboflow.com/hilmantm/traffic-accident-detection)
[](https://huggingface.co/spaces/hilmantm/detr-traffic-accident-detection) |