Fruit target detection method based on faster R-CNN Online publication date: Wed, 16-Feb-2022
by Guanghui Yin; Yuanmin Xie; Juntong Yun; Lichuan Ning; Ying Liu
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 21, No. 3, 2021
Abstract: With the rapid development of agricultural modernisation, fruit picking is becoming more and more automatic. The detection of fruit target by machine vision technology is the key to realise fruit automatic picking. In recent years, with the development of deep learning technology, target detection algorithm based on deep learning has gradually become a hot research topic, and the detection accuracy has been greatly improved. However, the shape and size of fruits in natural environment are different, and the light intensity changes at any time, which affects the detection accuracy to a certain extent. In this paper, aiming at the problem of fruit detection and location in natural environment, based on Fast R-CNN target detection model, a fruit detection and location method combining image processing and deep learning is proposed. The experimental results show that the combination of image processing and deep learning can achieve high detection accuracy and speed.
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