At present situation the human beings are faced many accidents during the road ways transportation. At the same time, they lose our life and valuable properties in those accidents. To avoid these problems, we design automatic speed limit detection and speed control system using Raspberry pi and deep learning.   The Digital image processing plays important role in the sign capturing and detection system. The image processing algorithms to takes the necessary action for resizing the captured signs. The objective of the proposed work is to recognize the speed limits on speed-limit signs automatically using pi camera and raspberry pi zero 2 W.   The proposed system will get the image of the real world from the Pi camera which is interfaced to the raspberry pi and deep learning are used to detect the speed limit of road signs. We can create a data set into the raspberry pi. This data set contains a greater number of speed limit signs. The raspberry pi processor takes the input from pi camera and it will compare with the data set and based on that it can identify the speed limit sign will be display on LCD module and also control the vehicle speed accordingly. If the system detects (20km, 30km, 40km) speed signs it will activate the buzzer for alerts. Here DC motor works as vehicle. So, this system is able to take action and reduces the chances of human errors like driver mistakes that results road accidents. The coding for this whole system is in python and for image processing we are using deep learning.

   

The major features of this project are:

• Camera based automatic speed limit detection using deep learning.
• The main objective of proposed system is automatic speed sign detection and vehicle speed control system.
• Alert speed sign detection using BUZZER.
• This system avoids the road accidents.

The major building blocks of this project are:

• Power Supply.
• Raspberry pi zero 2 W.
• Pi camera.
• LCD display.
• Buzzer.
• DC motor with driver.

Software’s used:

• Linux OS
• PYTHON LANGUAGE.
• Deep learning for image processing.

     

     

Block Diagram:    

     

 

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