Accident-Detection
Overview
Our main goal of this project is to use deep learning and computer vision to detect accidents on dashcam and report it to nearby emergency services with valid accident images.
Challenges
Our main challenge was to gather accident images and videos and manually categuorize images into accient and non-accident frames
To design a deep convolutional neural networks model for this project.
Limited hardware resorces like GPU's.
Team Members
1: Manoj Pawar Sj Github LinedLn
2: Manjunath Inti Github LinedLn
3.Bharat Kaushik Github LinedLn
4.DIkshita Basu Github LinkedLn
5.Bikash Singha Github LinkedLn
6.Rema Rose Toppo Github LinkedLn
Model Overview
1 . For this project we have tweaked Densenet-161 architecture
Dense Convolutional Network (DenseNet), connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections - one between each layer and its subsequent layer - our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.
The 1-crop error rates on the imagenet dataset with the pretrained model are listed below.
Model structure Top-1 error Top-5 error
densenet121 : 25.35 : 7.83
densenet169 : 24.00 : 7.00
densenet201 : 22.80 : 6.43
densenet161 : 22.35 : 6.20
Prerequisite
Download anaconda from here https://www.anaconda.com/distribution/#download-section
- Pytorch
conda install pytorch torchvision cudatoolkit=10.1 -c pytorch
- OpenCV
conda install -c conda-forge opencv
- Dataset of accident/non-accident images
https://drive.google.com/open?id=1o0D7vnGUZHS72is6n1jV1ge2BDfObzVi
- Pretrained Model binary file
https://drive.google.com/open?id=1AnJSogx65iyfIG0cSm5D15xfTGJzst8d
A proper php-language environment like xampp,remove htdocs folder and replace that with htdocs in this repo
Clone or Download this repo
git clone https://github.com/manojpawarsj12/accident-detection.git
DEMO
1.accident
2.Non-accident
Train
Go to bash/cmd and type
python train.py
Tensorboard visual
1. Traning set2. Validation set3.Number of corercts v/s epochs
Test/Accuracy
Go to bash/cmd and type
python test.py
Test on video
python evaluate.py
Test on Webcam
python livewebcam.py
Result
The model reaches a classification accuracy of 86.00% accuracy on a randomly sampled test set, composed of 20% of the total amount of video sequences from our dataset. Will re-train this model when we have a good GPU and somre data .