Medical Image Segmentation using Cuckoo Search Optimization I Matlab

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This project aims to design and implement a deep learning-based vision assistant module for the visually impaired.

This project investigates methods and procedures to construct an efficient system to assist blinds in their everyday life. In particular, various technologies that can be utilized to build a wearable system are examined. The machine vision and the communication component of blind navigation and guidance are designed not only to map the surrounding environment but also to determine a safe path to the desired destination. This work highlights the importance and also provides instructions to blinds for efficient navigation and safe guidance by incorporating objects/pedestrians in real-time.

In this project, Object recognition is done by the Pre-trained model MobileNet for recognizing the object with more than 95% accuracy. The model is trained with more than lakhs of images to recognize the object. An object such as a Person, chair, TV Monitor, etc. USB Camera is interfaced with the Raspberry Pi for this application. It can also be done using IP Webcam or Pi camera.

Existing system:

In the existing system, object recognition can be done using any of the Image processing Algorithms like SIFT, or SURF. But that kind of technique has lots of limitations, make more difficult to recognize the object.

Proposed System:

In the proposed system, deep learning is used. Among that Convolutional Neural Network is used with pre-trained model MobileNet to recognize the object with more accuracy in real-time

Connection Description:

Raspberry Pi is booted with the SDCard, with libraries installed like Keras, Tensorflow backend, NumPy, etc. USB camera is interfaced with the Raspberry pi to make it a real-time object recognition application.

Project Description

In this deep learning project, the Pre-trained model is used, Its accuracy is more than 90%. It can also be customized to recognize other objects using Transfer learning. This MobileNet is depthwise separable convolution, reducing the number of parameters. It is more suitable for vision-based applications where there is less performance power of the system

Hardware Required

  • Raspberry Pi
  • USB Camera

Software Required

  • Raspbian OS with libraries installed
  • SD Card Formatter
  • Etcher


[1] Krizhevsky A., Sutskever I., Hinton G. E. Imagenet classification with deep convolutional neural networks //Advances in neural information processing systems. ? 2012. ? ?. 1097-1105

[2] Simonyan K., Zisserman A. Very deep convolutional networks for large-scale image recognition //arXiv preprint arXiv:1409.1556. ? 2014.

[3] Szegedy C. et al. Going deeper with convolutions. ? Cvpr, 2015.

[4] He K. et al. Deep residual learning for image recognition //Proceedings of the IEEE conference on computer vision and pattern recognition. ? 2016. ? ?. 770-778.

[5] Ren S. et al. Faster r-CNN: Towards real-time object detection with region proposal networks //Advances in neural information processing systems.? 2015. ? ?. 91-99.

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