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Automatic inspection machine for maize kernels based on deep convolutional neural networks.
- Source :
-
Biosystems Engineering . Feb2019, Vol. 178, p131-144. 14p. - Publication Year :
- 2019
-
Abstract
- Maize inspection is an important and time-consuming task in the domain of food engineering. The human-based inspection strategy needs to be brought up to date with the rapid developments in the maize industry. In this paper, an automatic maize-inspection machine is proposed. Our proposed machine integrates several new designs in terms of both hardware and software components. First, a gravity-based dual-side camera design expands the machine's field-of-view to evaluate maize kernels more thoroughly. Second, touching kernels are pre-processed using a new k-means clustering guided-curvature method, which can improve the robustness of our machine. Next, a deep convolutional neural network, which has shown promise for application in image processing, is embedded into the system to evaluate maize kernels. In this work, the ResNet, which is a deep convolutional neural network architecture, was trained by fine-tuning with 1632 images. It achieved a 98.2% prediction accuracy for 408 test images, which outperforms existing approaches. Highlights • Design a dual-camera based synchronised maize inspection machine. • Applied an improved background removal method. • Propose a K-means-guided curvature method to segment the touching kernels. • Integrate deep convolutional neural network in the maize inspection machine. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15375110
- Volume :
- 178
- Database :
- Academic Search Index
- Journal :
- Biosystems Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 134049194
- Full Text :
- https://doi.org/10.1016/j.biosystemseng.2018.11.010