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22 April 2022 Multi-type fruit picking image recognition method based on deep learning
JinDe Huang, HuiHong Lan
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Proceedings Volume 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021); 121741G (2022) https://doi.org/10.1117/12.2629192
Event: International Conference on Internet of Things and Machine Learning (IoTML 2021), 2021, Shanghai, China
Abstract
In order to solve the problems of low correct recognition rate, high false recognition rate, long recognition time and poor image recognition effect in traditional methods, a multi-type fruit picking image recognition method based on deep learning is proposed. SVM is used to classify the image, the segmentation method based on statistical pattern recognition is used to segment the image, and the image is denoised according to mathematical morphology. According to the results of recognition, segmentation and denoising, the deep convolution neural network in deep learning technology is used to recognize many kinds of fruit picking images. The experimental results show that the error recognition rate of this method is low, the correct recognition rate is high, the recognition time is short, and the recognition effect is good, which fully verifies the application value of this method.
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JinDe Huang and HuiHong Lan "Multi-type fruit picking image recognition method based on deep learning", Proc. SPIE 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021), 121741G (22 April 2022); https://doi.org/10.1117/12.2629192
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KEYWORDS
Image segmentation
Convolution
Image processing
Facial recognition systems
Computing systems
Image classification
Image enhancement
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