Article: Defect detection method of Chinese mitten crab based on improved EfficientVit Journal: International Journal of Simulation and Process Modelling (IJSPM) 2025 Vol.22 No.1/2 pp.47 - 59 Abstract: An improved EfficientVit-based defect detection method for Chinese mitten crabs (<i>Eriocheir sinensis</i>) is proposed to enhance detection efficiency and accuracy while reducing human interference. A dataset of intact and defective crab images was constructed to improve the model's adaptability. The squeeze-and-excitation (SE) module and mix of experts hidden (MoH) module were integrated into the EfficientVit model to strengthen its feature extraction ability and sensitivity to subtle differences. The ReLU activation function was replaced with the parametric rectified linear unit (PReLU) activation function to address the 'dying neuron' issue and improve the model's sensitivity to negative inputs. The improved model achieved an accuracy of 97.34%, precision of 97.35%, recall of 97.34%, and F1-score of 97.34%, showing significant improvements over the baseline model. Future work will focus on optimising model performance and exploring its adaptability and generalisation in different environments. Inderscience Publishers - linking academia, business and industry through research

Title: Defect detection method of Chinese mitten crab based on improved EfficientVit

Authors: Guangyu Mu; Heng Zhang; Zhongxiang Fei; Guodong Gao; Haiguang Zhang

Addresses: Mechanical and Power Engineering College, Dalian Ocean University, Dalian 116023, China ' Mechanical and Power Engineering College, Dalian Ocean University, Dalian 116023, China ' Mechanical and Power Engineering College, Dalian Ocean University, Dalian 116023, China ' Navigation and Shipbuilding Engineering College, Dalian Ocean University, Dalian 116023, China ' Mechanical and Power Engineering College, Dalian Ocean University, Dalian 116023, China

Abstract: An improved EfficientVit-based defect detection method for Chinese mitten crabs (Eriocheir sinensis) is proposed to enhance detection efficiency and accuracy while reducing human interference. A dataset of intact and defective crab images was constructed to improve the model's adaptability. The squeeze-and-excitation (SE) module and mix of experts hidden (MoH) module were integrated into the EfficientVit model to strengthen its feature extraction ability and sensitivity to subtle differences. The ReLU activation function was replaced with the parametric rectified linear unit (PReLU) activation function to address the 'dying neuron' issue and improve the model's sensitivity to negative inputs. The improved model achieved an accuracy of 97.34%, precision of 97.35%, recall of 97.34%, and F1-score of 97.34%, showing significant improvements over the baseline model. Future work will focus on optimising model performance and exploring its adaptability and generalisation in different environments.

Keywords: Chinese mitten crab; EfficientVit; defect detection; attention mechanism; activation function.

DOI: 10.1504/IJSPM.2025.148289

International Journal of Simulation and Process Modelling, 2025 Vol.22 No.1/2, pp.47 - 59

Received: 10 Feb 2025
Accepted: 02 Apr 2025
Published online: 01 Sep 2025 *

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Title: Defect detection method of Chinese mitten crab based on improved EfficientVit

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