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Deep Learning-Based Apple Defect Detection with Residual SqueezeNet

Title
Deep Learning-Based Apple Defect Detection with Residual SqueezeNet
Authors
Alam, M. D. NurUllah, IhsanAl-Absi, Ahmed Abdulhakim
Issue Date
2020-07-08
Citation
International Conference on Smart Computing and Cyber Security: Strategic Foresight, Security Challenges and Innovation, SMARTCYBER 2020, 127-134
Type
Conference
ISBN
9789811579899
ISSN
2367-3370
Abstract
Apple defect detection using hyperspectral image has become an interesting research focus since the last decade. It is important for wide range of applications, such as agricultural sector, food processing, and automatic fruits grading system. However, this task is a challenging one due to calyx and stem, their different types and orientation, and similar in their visual appearance. The proposed method based on a deep learning approach using SqueezeNet architecture. However, the apple images are extracted and fed into a deep network for training and testing. The proposed SqueezeNet architecture utilizes convolution neural network to regress a bypass connection between the fire modules across the images. It has been evaluated our own created dataset. The excremental result shows that our proposed methods are efficient and effective. The general detection rate was 92.23%. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
URI
http://hdl.handle.net/20.500.11750/12918
DOI
10.1007/978-981-15-7990-5_12
Publisher
International Conference on Smart Computing and Cyber Security (SmartCyber)
Files:
There are no files associated with this item.
Collection:
ETC2. Conference Papers


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