Abstract
Fruit detection is of great significance in the agriculture. Recently, deep neural network has been widely studied in fruit detection. In our paper, we present a new approach of fruit detection, which uses Faster R-CNN based on deep network AlexNet. Our aim is to develop a fast, accurate and convenient model to detect and classify the fruit. Data augmentation is also used to increase the fruit dataset and avoid the overfitting in some degree. We use the fruits-360 dataset to perform the experiments.
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Acknowledgements
This study was supported by the Research Program Foundation of Minjiang University under Grants No. MYK17021 and supported by the Major Project of Sichuan Province Key Laboratory of Digital Media Art under Grants No. 17DMAKL01 and supported by Fujian Province Guiding Project under Grants No. 2018H0028 and supported by National Nature Science Foundation of China (Grant number: 61871204). We also acknowledge the solution from National Natural Science Foundation of China (61772254), Key Project of College Youth Natural Science Foundation of Fujian Province (JZ160467), Fujian Provincial Leading Project(2017H0030), Fuzhou Science and Technology Planning Project (2016-S-116), Program for New Century Excellent Talents in Fujian Province University (NCETFJ) and Program for Young Scholars in Minjiang University (Mjqn201601).
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Ma, L., Zhang, F., Xu, L. (2019). Fruit Detection Using Faster R-CNN Based on Deep Network. In: Zhao, Y., Wu, TY., Chang, TH., Pan, JS., Jain, L. (eds) Advances in Smart Vehicular Technology, Transportation, Communication and Applications. VTCA 2018. Smart Innovation, Systems and Technologies, vol 128. Springer, Cham. https://doi.org/10.1007/978-3-030-04585-2_23
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DOI: https://doi.org/10.1007/978-3-030-04585-2_23
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