To enter the room user, need to shows the APP screen into the pi camera. Pi camera is uses to monitor the APP screen by using image processing OpenCV.  If the pi camera detects green color text into the APP, this data process to the raspberry pi then raspberry pi generates the signal to unlock the door long with green LED indication. If the pi camera detects red color text into the APP, this data process to the raspberry pi then raspberry pi generates the signal to lock the door and active the buzzer along with red LED indication. The status of the project will display on LCD module. In this we are using servo motor works as door.

In this project we are using python language to write the program. Python is an interpreter, object-oriented, high-level programming language with dynamic semantics. Python’s simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse.

The processor is programmed in python language which intelligently performs the specific task. Here, the processor gets input from pi camera which is attached to the API port of raspberry pi.

The major features of this project are:

1. Using Raspberry pi zero 2W processor to achieve this task.
2. Using pi camera and openCV image processing to detect the color text on screen.
3. Audible alerts using Buzzer.
4. Visible alerts using LCD display.
5. Automatic door control based on vaccination doses.

The major building blocks of this project:

• Power supply.
• Raspberry Pi3 Processor.
• SD Card.
• Pi camera.
• Servo motor as door.
• LED indicators.
• Servo Motor.
• LCD display.
• Buzzer.

Software’s used in the project:

1. Python Programming.
2. OpenCV Image processing.
3. Embedded Linux operating system.
4. Express SCH for Circuit design.

Block Diagram:

video:

Echo Vision is a novel assistive device designed to enhance the mobility and spatial awareness of visually impaired individuals. The system integrates Raspberry Pi 3, a Pi Camera, an SD card, a rechargeable battery, an ultrasonic sensor, a headset, and LED indicators into a traditional blind stick. The main controlling system is in this project is raspberry pi 3.

When the user switch on the device Pi Camera captures real-time images of the surroundings, which are processed by the Raspberry Pi 3 using Open CV algorithms. The processed images are then converted into audio feedback, providing the user with auditory information about the environment.

The ultrasonic sensor further enhances the system by detecting obstacles in close proximity and alerting the user through the headset. The buzzer serves as an auditory alert system, providing feedback to the user about obstacles detected by the ultrasonic sensor or other critical information. LED indicators provide additional visual cues for the user.

The device aims to empower visually impaired individuals by providing them with a comprehensive understanding of their surroundings, allowing for safer and more independent navigation.

The major features of this project are:

1. Automatic obstacle sensing.
2. Automatic object detection and classification.
3. Using Raspberrypi3 processor to achieve this task.
4. Using pi camera and openCV Image processing to detect the objects
5. Using Ultrasonic sensor to detect the obstacle.
6. Voice alerts for obstacle detection.
7. Voice announces for object detection.
8. Using headsets for audible announcements.
9. Led for additional visuals for user.

The major building blocks of this project:

• Battery power supply.
• Raspberry Pi3 Processor.
• SD Card.
• Ultrasonic sensor.
• Pi camera.
• LED indicators.
• Head set.
• Buzzer.

Software’s used in the project:

1. Python Programming.
2. OpenCV Image processing.
3. Embedded Linux operating system.
4. Express SCH for Circuit design.

video:

The project aims in designing robust railway crack detection scheme (RRCDS) using Machine learning which avoids the train accidents. Raspberry pi and pi camera-based railway track detection system. And also, this system capable of GPS module for location tracking. If the robot detect abnormality on tracks, it can capture that image and sending this image into the cloud.

The GPS is the acronym for Global positioning system. This GPS receiver is capable of identifying the location in which it was present in the form of latitude and longitudes. This information is very useful and can be processed for alerting the boat drivers. The GPS gives the data received from the satellites. For this information the GPS communicates with at least three satellites in the space.

In this Project presents an automotive localization system based on robot using GPS and cloud services. The system permits localization of the automobile and transmitting the position to the authorities on their cloud service. The project consists of an automatic Robust robot system which is interfaced with DC motors and relay as DC motor driver. This project consists of raspberry pi 3 and pi camera for capturing and sensing image to the respective authorities. This project contains machine learning CNN image processing technology is used to images obtained from the tracks in order to classify them as normal or suspicious.SD card is providing the initial storage of operating system to the raspberry pi. To achieve this task raspberry pi loaded program written in embedded C language.

The main objectives of the project are:

• Automated railway TRACK CRACK detection using machine learning CNN algorithm.
• GPS based location tracking system.
• IoT based cloud storage system.

The major building blocks of this project are:

• Rechargeable Battery.
• Raspberry Pi3 Processor.
• SD Card.
• Pi camera.
• GPS Module.
• LCD Display.
• Buzzer.
• LED indicators.
• DC motors with L293d moor driver.
• Robot chassis.

Software’s used:

1. Raspbian OS.
2. Python Language.
3. Machine learning image processing (CNN) algorithm.

Block Diagram:

video:

This system to detect the object using image processing algorithm which is written in embedded Linux platform. All images like table, chair, bottle…..we will prepared dataset and stored into the raspberry pi and trained for preprocessing. This system uses Ultrasonic sensor to detect the nearest obstacle and announce the voice through speaker.

OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. It mainly focuses on image processing; video capture and analysis including features like person detection and object detection.

In this project we are using python language to write the program. Python is an interpreter, object-oriented, high-level programming language with dynamic semantics. Python’s simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse.

The controlling device of the whole unit is a Raspberrypi3 processor to which input and output modules are interfaced. Raspberrypi3 has Broadcom BCM2837B0, Cortex-A53 (ARMv8) 1GB LPDDR2 SDRAM 1.4GHz 64-bit quad-core processor, dual-band wireless LAN, Bluetooth 4.2/BLE, faster Ethernet, and Power-over-Ethernet support (with separate PoE HAT).

The processor is programmed in PYTHON language which intelligently performs the specific task. Here, the processor gets input from the sensor and pi camera attached to the raspberry pi. This input is processed by processor and announce the voice appropriately.

The major features of this project are:

• 1. Automatic obstacle sensing.
  2. Automatic object detection and classification.
  3. Using Raspberrypi3 processor to achieve this task.
  4. Using pi camera and openCV machine learning to detect the objects
  5. Using Ultrasonic sensor to detect the obstacle.
  6. Voice alerts for obstacle detection.
  7. Voice announce for object detection.

The major building blocks of this project:

• Power Bank
• Raspberry Pi3 Processor.
• SD Card.
• Ultrasonic sensor.
• GPS
• Speaker
• Pi camera.
• LED indicators.

Software’s used in the project:

1. Python Programming.
2. OpenCV MACHINE LEARNING Image processing.
3. Embedded Linux operating system.
4. Express SCH for Circuit design.

video: