Real-Time ConvNext-Based U-Net with Feature Infusion for Egg Microcrack Detection

Chenbo Shi, Yuejia Li, Xin Jiang, Wenxin Sun, Changsheng Zhu, Yuanzheng Mo, Shaojia Yan and Chun Zhang ()
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Chenbo Shi: College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, China
Yuejia Li: College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, China
Xin Jiang: College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, China
Wenxin Sun: College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, China
Changsheng Zhu: College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, China
Yuanzheng Mo: College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, China
Shaojia Yan: College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, China
Chun Zhang: College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, China

Agriculture, 2024, vol. 14, issue 9, 1-19

Abstract: Real-time automatic detection of microcracks in eggs is crucial for ensuring egg quality and safety, yet rapid detection of micron-scale cracks remains challenging. This study introduces a real-time ConvNext-Based U-Net model with Feature Infusion (CBU-FI Net) for egg microcrack detection. Leveraging edge features and spatial continuity of cracks, we incorporate an edge feature infusion module in the encoder and design a multi-scale feature aggregation strategy in the decoder to enhance the extraction of both local details and global semantic information. By introducing large convolution kernels and depth-wise separable convolution from ConvNext, the model significantly reduces network parameters compared to the original U-Net. Additionally, a composite loss function is devised to address class imbalance issues. Experimental results on a dataset comprising over 3400 graded egg microcrack image patches demonstrate that CBU-FI Net achieves a reduction in parameters to one-third the amount in the original U-Net, with an inference speed of 21 ms per image (1 million pixels). The model achieves a Crack-IoU of 65.51% for microcracks smaller than 20 μ m and a Crack-IoU and MIoU of 60.76% and 80.22%, respectively, for even smaller cracks (less than 5 μ m), achieving high-precision, real-time detection of egg microcracks. Furthermore, on the publicly benchmarked CrackSeg9k dataset, CBU-FI Net achieves an inference speed of 4 ms for 400 × 400 resolution images, with an MIoU of 81.38%, proving the proposed method’s robustness and generalization capability across various cracks and complex backgrounds.

Keywords: poultry eggs; microcrack detection; lightweight model; semantic segmentation; deep learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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