An acoustic signal-to-image conversion integrated convolutional neural network model for egg crack detection
Küçük Resim Yok
Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Taylor & Francis Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
1. The presence of fractures or cracks in eggshells represent a significant risk in terms of food safety. Bacteria and viruses are likely to enter through these cracks, which increases the risk of food poisoning. Furthermore, deformations in the shell can compromise the integrity of the protective shell, rendering the egg more susceptible to environmental damage and accelerating deterioration.2. In order to mitigate these risks, a convolutional neural network (CNN) integrated into an acoustic signal to image conversion was developed as a crack detection system. Mechanical and electronic sub-systems were designed to generate non-destructive acoustic excitation on the eggshell and capture the resulting sound with a high-sensitivity microphone.3. The recorded 1 x 731-sample signals from 120 intact or cracked eggs were subjected to variational mode decomposition (VMD) to extract intrinsic mode functions (IMF). Subsequently, IMF were converted to greyscale images and classified using the proposed acoustic signal-to-image conversion and the lightweight CNN.4. The proposed model showed the capability (100%) to distinguish between intact and cracked eggs, including invisible micro-cracks.
Açıklama
Anahtar Kelimeler
Variational mode decomposition, defect detection, acoustic signal, convolutional neural network, eggshell crack detection
Kaynak
British Poultry Science
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
