Enhancing Road Safety with AI-Powered Accident Detection

Objective

The objective of this project is to develop an AI-driven system that detects accident scenes from images captured by CCTV footage. By leveraging advanced machine learning techniques, we aim to improve response times to road incidents, thereby enhancing overall road safety.

Data Sample

We utilized the Accident Detection from CCTV Footage dataset from Kaggle. This dataset contains annotated images from CCTV footage, showcasing various accident scenarios.

Sample Data

Here’s a sample from the dataset:

Image	Label
![Accident Image]	Accident

The images are categorized into "Accident" and "No Accident," which helps train the model to distinguish between accident scenes and normal traffic conditions.

Model Architecture

Our model employs a Vision Transformer (ViT) architecture, which is well-suited for image classification tasks. The key components of the model include:

Input Layer: Accepts images resized to a specified resolution.
Transformer Encoder Layers: Extract features through self-attention mechanisms, capturing spatial relationships.
Feedforward Neural Networks: Process the features and classify them into accident-related categories.
Output Layer: Provides the final classification probabilities for "Accident" and "No Accident."

Instructions for Running the Training Job

To run the training job, follow these steps:

Clone the repository:

git clone https://github.com/yourusername/accident-detection.git
cd accident-detection

vit-accident-image

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the accident classification dataset. It achieves the following results on the evaluation set:

Loss: 0.2027
Accuracy: 0.93
F1: 0.9301

Model description

label 0 : non-accident , label 1 : accident-detected

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.3546	2.0	100	0.2327	0.9184	0.9184
0.1654	4.0	200	0.2075	0.9388	0.9388
0.0146	6.0	300	0.2497	0.9388	0.9387
0.0317	8.0	400	0.2179	0.9286	0.9285
0.0192	10.0	500	0.2255	0.9286	0.9286

Framework versions

Transformers 4.30.0
Pytorch 2.2.1+cu121
Datasets 2.19.1
Tokenizers 0.13.3

tiya1012
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vit-accident-image