A Webcam and LabVIEW-based System for Efficient Object Recognition based on Colour and Shape Features

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Abstract
This paper proposes a system for efficient object recognition based on colour and shape features using a webcam and LabVIEW. The study aims to develop a model scale conveyor belt system for Lego brick sorting based on colour and shape. The proposed system uses the webcam and LabVIEW's graphical programming environment to capture and analyse images, extract colour and shape features, and perform object recognition. An Arduino microcontroller, integrated with LabVIEW software, is used to move the servo and motor of the conveyor belt sorting system based on the analysed image capture from the webcam. The proposed system has the potential to be applied in real-world applications such as the food industry, particularly in fruit and vegetable sorting which will help reduce the amount of time and labour needed for manual sorting and food waste by ensuring that only good quality produce is sold. Another potential real-world application for the proposed system is quality control and defect detection in the manufacturing industry. The system proposed in this paper can sort objects using object recognition based on the object's colour and shape features, with the overall system average reliability percentage is 90%. Overall, the system is applicable in real-world applications if the limitations mentioned are overcome and improved by integrating with the Internet of Things (IOT) for a long-range monitoring system.