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Publication Type : Conference Proceedings
Publisher : 2019 International Conference on Cutting-edge Technologies in Engineering (ICon-CuTE)
Source : 2019 International Conference on Cutting-edge Technologies in Engineering (ICon-CuTE) (2019)
Keywords : Agricultural products, bounding boxes, classification, Complex background, daily production, dark flow, detection, Feature extraction, Food products, Fruits, green vegetables, image classification, Object Detection, OpenCV, tensor flow version, vegetable classification, vegetable images, vegetable recognition, Vegetables, YOLO, You only look once algorithm
Campus : Bengaluru
School : Department of Computer Science and Engineering, School of Engineering
Department : Computer Science
Year : 2019
Abstract : Vegetable detection and classification is a challenging objective in daily production and use, and the complexity increases when other parameters such as shape, size and color are taken into consideration. In this paper, we define a methodology that will detect and classify three different green vegetables of different sizes. This method involves the use of Tensor Flow, Dark flow which is a Tensor flow version of You only look once (YOLO) algorithm, OpenCV. To train the desired network, several various vegetable images were fed into the network. The images were pre-processed before training by drawing bounding boxes around the vegetable manually using OpenCV. The main algorithm responsible for the detection and classification is YOLO. This method provides a faster and smarter way to identify an object in the given image or video. Once the network is trained, the test input is passed in to the network. Once the input is provided to the network, the output will display bounding boxes around the recognized vegetable and label it with its predicted class category with accuracy of 61.6 percent.
Cite this Research Publication : Sachin C., N. Manasa, Vicky Sharma, and Nippun Kumaar A A, “Vegetable Classification Using You Only Look Once Algorithm”, 2019 International Conference on Cutting-edge Technologies in Engineering (ICon-CuTE). 2019.