This repository contains an implementation of YOLOv5s, details of which can be found here.
With just little aid to the visually impaired people existing currently, there is a need to implement a device that helps them in their daily activities. The existing systems such as Screen Reading software and Braille devices help visually impaired people to read and have access to various gadgets but these technologies become are not useful in the case of navigation. Our system will facilitate visually impaired people around the globe. DolphinEye is made with an aim of helping a person with detecting the object in front of them, even with impaired eyesight and without the need of a companion. We present a method, which uses object detection on the live stream of videos. The resultant object or person is then transmitted to the impaired person in the form of signal. The goal is to provide inexpensive solution to the visually impaired and make their life better and self-sufficient.
We have used the pretrained weights of the Microsoft COCO dataset (Microsoft Common Objects in Context) which is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.
This project uses a PyTorch implementation of YOLOv5s. YOLOv5 is a family of compound-scaled object detection models trained on the COCO dataset, and includes simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite. The architecture consists of:
- Model Backbone - CSPNet are used as a backbone to extract rich informative features from an input image
- Model Neck - Model Neck is mainly used to generate feature pyramids. PANet is used for as neck to get feature pyramids
- Model Head - The model Head is mainly used to perform the final detection part. It applied anchor boxes on features and generates final output vectors with class probabilities, objectness scores, and bounding boxes.
The three main tasks of our project are:
- Object Detection using the YOLOv5 architecture
- Calculating the depth of the objects
- Sending warning messages in case object is too close
- Object Detection using the YOLOv5 architecture
- Model implemented is YoloV5s, which is the smallest version of YoloV5.
- Model architecture is defined in a YAML file, which clearly mentions all the layers and their arguments.
- All the model blocks are defined in the files in model folder, along with the main yolo.py file which parses the yaml and creates the model.
- This implementation loads in the pretrained weights from YoloV5 repo.
- Calculating the depth of the objects
- The inspiration for depth calculation was taken from here
- It essentially utilises a focal distance relationship to calculate how far an object is.
- Sending warning messages in case object is too close
- In case the distance falls below a threshhold, we've utilised the google tts API to make an mp4 of a simple warning (STOP) which is played back to the user.
- [NOTE] This implementation works ONLY for a real time stream
The code by default will only run on a video stream. To use the repo and run inferences, please follow the guidelines below:
-
Clone the Repository:
$ git clone https://github.com/Ananya-Jha-code/DolphinEye.git -
Enter the directory:
$ cd DolphinEye -
Install the requirements:
$ pip install -r requirements.txt -
For running on CLI, use the inference file as follows:
$ python inference.py
We can see how the distance of the chair in bottom left corned goes from around 7000 to around 5000. Based on a threshhold, the application will issue a warning if the distance is less than the threshhold.
- Finish streamlit frontend
- Integrate the model with a flutter app
- Improve accuracy for distance measurement
We are always open to any ideas about how to improve the project, or add features, or discuss anything about it! Feel free to reach out to any of the contributors listed below. For exact guidelines on how to contribute, check out this document.