Abstract
Due to the variety of fruit, fruit volume estimation is quite challenging. In this paper, we present a deep neural network based approach, DeepVol, to joint detection and volume estimation in a framework. The proposed architecture consists two independent parts: SSD-based fruit detector and ResNet-based volume regressor. To train the network models, a fruit dataset involving fruit volume and images is collected as a benchmark to verify the volume estimation framework. This method is simple and convenient in practical applications, owing to its requiring no conventional camera calibration and only single image as input. Experimental results demonstrate that our approach is robust to different surroundings, and promising in calorie measurement and unmanned stores.
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Li, H., Han, T. (2018). DeepVol: Deep Fruit Volume Estimation. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_33
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