Quality Grading of Dried Abalone Using an Optimized VGGNet

Author:

Zhong Yansong1,Lin Hongyue1,Gan Jiacheng2,You Weiwei23,Chen Jia14,Zhang Rongxin5

Affiliation:

1. School of Electronic Science and Engineering (National Model Microelectronics College), Xiamen University, Xiamen 361005, China

2. College of Ocean and Earth Sciences, Xiamen University, Xiamen 361005, China

3. Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, Xiamen University, Xiamen 361102, China

4. Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen 361005, China

5. Key Laboratory of Underwater Acoustic Communication and Marine Information, Xiamen University, Xiamen 361005, China

Abstract

As living standards have improved, consumer demand for high-quality dried abalone has increased. Traditional abalone grading is achieved through slice analysis (sampling analysis) combined with human experience. However, this method has several issues, including non-uniform grading standards, low detection accuracy, inconsistency between internal and external quality, and high loss rate. Therefore, we propose a deep-learning-aided approach leveraging X-ray images that can achieve efficient and non-destructive internal quality grading of dried abalone. To the best of our knowledge, this is the first work to use X-ray to image the internal structure of dried abalone. The work was divided into three phases. First, a database of X-ray images of dried abalone was constructed, containing 644 samples, and the relationship between the X-ray images and the internal quality of the dried abalone was analyzed. Second, the database was augmented by image rotation, image mirroring, and noise superposition. Subsequently, a model selection evaluation process was carried out. The evaluation results showed that, in a comparison with models such as VGG-16, MobileNet (Version 1.0), AlexNet, and Xception, VGG-19 demonstrated the best performance in the quality grading of dried abalone. Finally, a modified VGG-19 network based on the CBAM was proposed to classify the quality of dried abalone. The results show that the proposed quality grading method for dried abalone was effective, achieving a score of 95.14%, and outperformed the competitors, i.e., VGG-19 alone and VGG-19 with the squeeze-and-excitation block (SE) attention mechanism.

Funder

Science and Technology Projects of Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province

National Nature Science Foundation of China

National Funding Program for Postdoctoral Researchers

China Postdoctoral Science Foundation

Publisher

MDPI AG

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