Implementation of Fruit Quality Classification System using YOLO Algorithm

• Main Authors: Liu,Chun-Yu, 劉峻瑜
• Other Authors: Chen,Ming-Chih
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/2chdzs

碩士 === 國立高雄科技大學 === 電子工程系 === 107 === The thesis presents a proposed system that uses YOLO (You Only Look Once)-V3 algorithm, IOU (Intersection over Union) tracking method, and CNN (Convolutional Neural Network) classifier to identify the external quality of fruits. The system mainly uses the YOLO-V3 algorithm to perform the fruit detection process, uses the IOU tracking algorithm to track the designated fruits continuously, and identifies fruits during the tracking processes. It can pick up good fruits through controlling the switched gap of conveying platform. It performs the software programs on the Jetson TX2 embedded development platform and uses the STM32 processor to control the switched gap. The proposed system can detect small and round fruits under an effective development process. To improve the efficiency of system, a graphic user interface is also designed to control , collect data, evaluate models,and monitor the entire system operation. The experimental results show that our proposed system can achieve up to 88% of the accuracy rate, 75% of the mean Average Precision (mAP) after testing 4,500 images of fruits.