Research Article
Study on Egg Freshness Detection Based on Inception and Attention
@INPROCEEDINGS{10.1007/978-3-030-97124-3_47, author={Min-lan Jiang and Li-yun Mo and Pei-lun Wu}, title={Study on Egg Freshness Detection Based on Inception and Attention}, proceedings={Simulation Tools and Techniques. 13th EAI International Conference, SIMUtools 2021, Virtual Event, November 5-6, 2021, Proceedings}, proceedings_a={SIMUTOOLS}, year={2022}, month={3}, keywords={Egg freshness classification Inception CBAM}, doi={10.1007/978-3-030-97124-3_47} }- Min-lan Jiang
Li-yun Mo
Pei-lun Wu
Year: 2022
Study on Egg Freshness Detection Based on Inception and Attention
SIMUTOOLS
Springer
DOI: 10.1007/978-3-030-97124-3_47
Abstract
Egg freshness is an important economic index to measure egg quality, and it is also the main factor affecting egg sales. In this paper, aiming at the problem of small number of training and testing samples in current research, a sample collection device was set up, and 1173 pictures of egg samples with three different levels of freshness were collected, which greatly expanded the number of samples. On this basis, aiming at the problems of strong subjectivity and low accuracy of the obtained model when extracting features manually in the current research, the CBAM module is used in combination with the Inception module to construct a network model, and attention mechanism was introduced to assign adaptive weights to the collected multi-scale features, which further improved the accuracy of the network and the problem of network over-fitting, and establishes a high-precision egg freshness detection model. The test results showed that the average test accuracy of GoogLeNet-A reaches 94.05%, and the highest test accuracy reaches 98.44%. At the same time, compared with other existing deep learning models, the experimental results showed that the detection model proposed in this paper has the highest accuracy, which provided a new idea and method for egg freshness detection.
