Helmet Mask and Distance detection using AI
Helmet, mask and social distancing is detected using artificial intelligence
You can check the demo under here
Scene Text Recognition from Images
Scene Text Recognition from Images
Scene Text Recognition from Images is implemented in Python
Project demo Video
Bird Species Detection using Deep Learning Algorithm
Bird Species Detection using Deep Learning Algorithm with best accuracy
Bird Species Detection using Deep Learning Algorithm with best accuracy has bee achieved using VGG16 CNN model for training and classification. This project is implemented in Python programming. Bird species dataset containing 325 classes of birds are take for this work. The dataset contained train, test and validation data. The detection is bird species has been achieved with good accuracy.
The following screen shows the performance of detection and classification based on test input
Performance of Bird Species Detection and Classification Output
What is the advantages of this project?
In this project, we can identify the bird species for selected birds as shown above. Moreover, in this work, user can choose a bird and classify its species using our trained model.
You can watch the Bird species classification using VGG16 demo video under following
Are you interested to check more VGG 16 implementation? some of the project is given below
stock price prediction in machine learning and Deep learning
Stock price prediction using machine learning and deep learning
Stock price prediction using machine learning and deep learning is the python project implemented with Decision tree algorithm for ML and Convolutional Neural Network for DL. This also gives stock price forecast for next 5 days.
How to Download dataset for Stock Price prediction?
We have used NSE real time data and it can be download with the use of mpl_finance library through python code. The data we have downloaded from 2000 to till date. We can able to download any stock data depending on the symbol fiven by NSE India.
What is the algorithm Used for this project?
The algorithm used are Decision Tree and CNN algorithm
What is the output of the project?
Stock price prediction is the ultimate output of the project. Whereas we also evaluated the performance of algorithm by measures such as MAE, MSE, RMSE and R-Squared error values.
What is the application of Stock price prediction?
Stock price prediction is useful for traders and investors for the predicting and forecasting the price. The volatility nature of stock price may reduce the performance of the algorithm.
What is the advantages of this project?
In this project, any stock price prediction can be performed. The stock symbol represented by NSE should be given. Thus any NSE stock can be given and download real time data for prediction.
Demo of the project can be viewed below
Cross Walk detection from Images and Videos for Pedestrian to cross Zebra crossing
Cross Walk detection from Images and Videos
Cross Walk detection from Images and Videos for pedestrian to cross Zebra crossing in roads. This application is developed using pre-trained model. The detection is done for cross walks on the roads.
What is the use of this application?
This application can detect cross walks on the roads, which in turn detecting is useful for autonomous vehicle to find the cross walk and control the speed of vehicle.
What is the output of the project?
We detect the person among the cross walk and ensure that they are cross through zebra crossing. Cross walks is detected on the road. The output sample is given below.
Demo of the project is available below
Looking for similar project? Do check our
Object detection and voice alert for visually impaired
Object detection with YOLO-V2 and voice alert for visually impaired is a Python project implemented with YOLO pre-trained model. This uses webcamera, it can used either externally or through laptop camera for demonstration. The system generates the voice alert and movement in direction such as ‘Right’ or ‘Left’ is also specified on the output. The object detected for the demonstration purpose on the given demo video is Person and Mobile phone. Whenever the objects moves, it gives voice alert to user.
How Object detection can be implemented with YOLO?
YOLO is the pre-trained model, which can detect more than 9000 classes of objects. The pre-trained weight files and class name files are loaded to the project directory. Opencv is the library used for YOLO object detection. More information of YOLO object detection can be viewed at https://opencv-tutorial.readthedocs.io/en/latest/yolo/yolo.html
How to generate Voice alert for Object detection project?
Voice alert can be integrated to our application through Python code. GTTS (Google Text to Speech) is the library used generally for conversion of detected object as text value to Speech data. This is very fast in conversion and more reliable to use.
How to get Architecture diagram for Object detection project?
The following figure shows the architecture diagram of the object detection project. This diagram shows all modules implemented on this work. Input for the project is Video stream converted to image frames for object detection detection. Output of the project is detected object is shown with bounding boxes and voice output.
Request Us Project Abstract and PPT Call 9600095046
Demo Video of Object Detection project
Related Object detection project are as follows
Luggage detection and approval in Airports using CBIR -Content based Image retrieval
Secure Fee Management in Python
Secure Fee Management in Python is a web application developed with Flask, Python, MySQL, which has entities such as admin and Staff
Secure Fee Management in Python
Admin has the following features to do
- Add Student
- Add Staff
- View Report
- Fees details paid by students
Staff module has the following features to do
- View updated personal details
- View Fees report for students
The complete project demo is available in the following link
Speech Emotion detection using Deep learning
Top 8 Human Speech Emotion detection or recognition using deep learning algorithm
Top 8 Human Speech emotion detection is identified in this project. It useful to understand the human emotions when they deliver through speech. Emotion of 8 categories namely Calm, Happy, Sad, Angry, Fearful, Disgust, Surprised and Neutral. These emotions are considered with female and male audio “.wav” wave files are considered as dataset. The speech emotion detection dataset represents as follows
File name example 03-01-01-01-01-01-02
Audio-only (03)
Speech (01)
Neutral (01)
Normal intensity (01)
Statement “dogs” (02)
1st Repetition (01)
12th Actor (02)
Female, as the actor ID number is even
The dataset is trained with Convolutional Neural Networks (CNN) algorithm. More specifically, CNN1 is used for training and validation. The implementation includes live detection of emotion from audio file, some audio we tested from Youtube. There are 8 emotion is classified in this project. Speech emotion detection is more useful to identify the person’s emotion without seeing face. The source code is neatly written in Python for speech emotion recognition from audio files. The following image represents the result of emotion on real time detection from audio input.
You can Check the demo video below with complete implementation
Human emotions are of two types, speech and face emotion. Here we studied Speech emotion. Human face emotions are also discussed in our previous post, Check the link
A Particle Swarm Optimized Feature selection and Learning for Fault Classification in Web-Apps
Web attacks are considered to be serious attack as the users of website may not get proper services if web application got an attack. The proposed work aim to classify web attacks as malicious and normal. There are 2 datasets used in proposed work are
- HTTP dataset CSIS 2010
- FWAF dataset created by FSECURITY
Deep neural network DNN algorithm is used for this study.
The efficient feature selection algorithm applied in this proposed work is PSO (Particle Swarm Optimization). The hybrid model of web attack detection is implemented.
The model outperformed with highest accuracy of 99.5% using FWAF dataset
The dataset is classified as binary classification model using DNN algorithm and source code is available in Python. Keras and Tensorflow are used for deploying and building a deep learning model for web attack detection. The following output image shows the accuracy of the DNN algorithm.
Further, the working model of the proposed work is available in following demo
Such kind of intrusion detection and attack detection and classification is available in the following link and provided more information