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http://dx.doi.org/10.9717/kmms.2021.24.5.695

Research on Shellfish Recognition Based on Improved Faster RCNN  

Feng, Yiran (Dept. of Information Communication Engineering, Tongmyong University, Dept of Mechanical Engineering and Automation, Dalian Polytechnic University)
Park, Sang-Yun (Dept. of Education Innovation Support Team, Silla University)
Lee, Eung-Joo (Dept. of Information Communication Engineering, Tongmyong University)
Publication Information
Journal of Korea Multimedia Society / v.24, no.5, 2021 , pp. 695-700 More about this Journal
Abstract
The Faster RCNN-based shellfish recognition algorithm is introduced for shellfish recognition studies that currently do not have any deep learning-based algorithms in a practical setting. The original feature extraction module is replaced by DenseNet, which fuses multi-level feature data and optimises the NMS algorithm, network depth and merging method; overcoming the omission of shellfish overlap, multiple shellfish and insufficient light, effectively solving the problem of low shellfish classification accuracy. In the complexifier test environment, the test accuracy was improved by nearly 4%. Higher testing accuracy was achieved compared to the original testing algorithm. This provides favourable technical support for future applications of the improved Faster RCNN approach to seafood quality classification.
Keywords
shellfish; deep learning; Faster RCNN; recognition;
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