Description
Road Sign Recognition using Raspberry Pi and OpenCV
Abstract:
Traffic Sign Recognition (TSR) systems employ vehicle-mounted cameras that identify traffic signs while driving on the road. Typically, these systems recognize speed limit signs, stop signs, and warning signs such as pedestrian crossings, railroad crossings, etc. Their primary function is to inform the driver of recent traffic signs that may have been missed due to distraction or inattentiveness. A camera scans the roadside for signs. Real-time image processing software identifies, interprets, and displays them on a panel on the vehicle dashboard. TSR systems perform the following basic functions. This project runs on the Raspberry Pi platform.
The Raspberry Pi is a credit card-sized single computer or SoC that uses ARM1176JZF-Score. A system on a Chip is a method of placing all necessary electronics for running a computer on a single chip. It needs an Operating system to start up. SD/MMC card will act as a bootable hard disk. Road Sign Recognition using Raspberry Pi and OpenCV
Road Sign Recognition using Raspberry Pi and OpenCV
Existing System:
- No camera-based sign detection
- No indicator
- With controller application
Road Sign Recognition using Raspberry Pi and OpenCV
Proposed System:
Our proposed systems recognize speed limit signs, stop signs, and warning signs such as pedestrian crossings, railroad crossings,s, etc along with Raspberry pi.
Road Sign Recognition using Raspberry Pi and OpenCV
Advantages:
- Image signal detection
- Indicating through display
Block Diagram:
Block Diagram Explanation:
In this block diagram, the whole system is controlled by an Arm11 processor, and this processor is implemented on Raspberry Pi Board. So this board is connected with a monitor, camera, SD card, and IP connected through LAN. Those all components are connected by USB adaptors. Raspberry pi is the key element in the processing module which keeps on monitoring road signs by interfacing a USB camera in that application area. The first step is image detection than recognition. The output of the detection stage is a list of image bounding boxes, each containing a yet unrecognized traffic sign.
Hardware:
- Raspberry Pi
- Camera
- SD card
- Display unit
- Motor speed rotation
Software:
- Raspberry pi OS: Raspbian stretch
- Programming Platform: python 3 IDLE
- Programing language: python 3
- Library: OpenCV
Applications:
- Electrical applications
- Industrial applications
- RTOS applications
REFERENCES:
- O. Dabber et al., “An end-to-end system for crowdsourced 3D maps for autonomous vehicles: The mapping component,” in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., Sep. 2017, pp. 634–641.
- V. Badrinarayanan, A. Kendall, and R. Cipolla. (2015). “SegNet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labeling.” [Online]. Available: https://arxiv.org/abs/1505.07293
- J. Uhrig, M. Cordts, U. Franke, and T. Brox. (2016). “Pixel-level encoding and depth layering for instance-level semantic labeling.” [Online]. Available: https://arxiv.org/abs/1604.05096
- G. Lin, C. Shen, A. van den Hengel, and I. Reid. (2016). “Exploring context with deep structured models for semantic segmentation.” [Online]. Available: https://arxiv.org/abs/1603.03183
- R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in Proc. Comput. Vis. Pattern Recognit., Jun. 2014, pp. 580–587.
- W. Liu et al., “SSD: Single shot multibox detector,” in Proc. Eur. Conf. Comput. Vis., 2016, pp. 21–37.
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