https://doi.org/10.4081/jae.2024.1564
Multi-class segmentation of navel orange surface defects based on improved DeepLabv3+
Published: 20 February 2024
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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A multi-class segmentation model based on improved DeepLabv3+ is proposed to detect navel orange surface defects. This model aims to address the problems of the current mainstream semantic segmentation network, including rough edge segmentation of navel orange defects, poor accuracy of small target defect segmentation, and insufficient deep-level semantic extraction of defects, which will result in the loss of feature information. In order to improve semantic segmentation performance, the Coordinate Attention Mechanism is integrated into the DeepLabv3+ network. Additionally, the deformable empty convolution of the Atrous Spatial Pyramid Pooling structure replaces the dilated convolution, improving the network’s ability to fit and target irregular defects and shape changes. Furthermore, to achieve multi-scale feature fusion and enhance feature space and semantic information, a Bi-feature pyramid network-based feature fusion branch is added at the DeepLabv3+ encoder side. The experimental findings demonstrate that the improved DeepLabv3+ model improves the extraction capability of navel orange defect features and has better segmentation performance. On the navel orange surface defect dataset, the improved model’s average intersection ratio and average pixel intersection ratio accuracies are 77.32% and 86.38%, respectively, which are 3.81% and 5.29% higher than the original DeepLabv3+ network.
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How to Cite
Zhu, Y. (2024) “Multi-class segmentation of navel orange surface defects based on improved DeepLabv3+”, Journal of Agricultural Engineering, 55(2). doi: 10.4081/jae.2024.1564.
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