Project Overview
The Blind Object detection system has the capability to detect more than 80 objects categorized from our day to day lives. To name a few, the objects include pen, person, mug, book, mobile, chair, and more. The system efficiently works on the YOLO model to detect the objects within the range of 2-5 m away. The speech to text facility allows the user to input the name of the desired object to be detected. It is again supported by popular computer vision technology that focuses on enhancing the user experience.
Client Background
The India based client finds it necessary to get the solution to detect objects by using Blind Object Detection through AI. The client desires to spot instances of semantic objects. However, there can be multiple uses of the AI-driven object detection model with deep-learning techniques. The essential idea behind designing this model is to identify the predefined list of objects from the environment based on the input of database images.
Business Needs
Blind Objects Detection works under the influence of Artificial Intelligence and helps visually impaired people. It works as an assistive system to identify persons or objects for color-blind people to recognize the physical world. The software can be embedded in wearable apps that are useful for visually challenged people. The information enables the ability of self-orientation and helps to
map the direction to it.
The Challenge
One of the main challenges in preparing machine learning models for Blind Object Detection is to run the scripts. The scripts take more than long to derive the output. We have used 1000 images for each object to train the model using Artificial Intelligence technology. It is necessary to ensure that there are no conflicts between object detection while training the model. It not only shows the object but allows you to know where the object is placed in real-time. This functionality was very challenging to achieve.
Our Solution
Our team of highly-experienced Python developers has used YOLO object detection. It essentially works on deep learning and CNN (conventional neural networks) to achieve a large number of object recognition. The CNN evaluates the bounding boxes and class probabilities to predict the object from full images. To enable YOLO detection, we have employed OpenCV and Python. It promises to be one of the fastest object detection algorithms. As the system uses a single network, it can be used to detect end-to-end directly based on distance parameters.
Some of the considerable uses of this Blind Object Detection system are:
- The machine learning model based on the YOLO detector enables object detection.
- The system can be integrated on wearable applications.
- It can be implemented in applications to use it as an assistive system for impaired people.
- The single neural network technology allows the detection accuracy of the model.
- It has become popular due to its lighting speed object detection functionality.
Key Outcomes
- The series of images input enables real-time object detection with utmost accuracy.
- The trained model interprets the bounding boxes into an object detection.
- It offers great benefits for blind people or visually impaired customers.
- The system connects the semantic objects from the environment for detection.
