EXPLORING TRANSFER LEARNING AND CONVOLUTIONAL AUTOENCODER FOR EFFECTIVE KITCHEN UTENSILS CLASSIFICATION
Hashim Rosli1 , Rozniza Ali2*, Muhamad Suzuri Hitam3, Ashanira Mat Deris4 and Noor Hafhizah Abd Rahim5
1,2*,3,4,5Faculty of Computer Science and Mathematics, Universiti Malaysia Terengganu, Kuala Nerus, 21030 Terengganu
1This email address is being protected from spambots. You need JavaScript enabled to view it., 2*This email address is being protected from spambots. You need JavaScript enabled to view it., 3This email address is being protected from spambots. You need JavaScript enabled to view it., 4This email address is being protected from spambots. You need JavaScript enabled to view it., 5This email address is being protected from spambots. You need JavaScript enabled to view it.
ABSTRACT
Effective classification of kitchen utensils is crucial for advancing assistive technologies and enhancing daily living for individuals with visual impairments. This study investigates the use of transfer learning and convolutional autoencoders to improve classification accuracy. We integrate pre-trained networks into an autoencoder framework to enhance feature extraction and image reconstruction. Models including ResNet50, DenseNet121, and their autoencoder variants were evaluated using precision, recall, accuracy, Structural Similarity Index Measure (SSIM), and Peak Signal-to-Noise Ratio (PSNR). Results show that DenseNet121 outperforms ResNet50 with a classification accuracy of 72% and shorter training time. When combined with autoencoders, DenseNet121-Autoencoder achieves the highest classification accuracy of 76% and superior image reconstruction quality, as indicated by higher SSIM and PSNR scores. This improvement highlights DenseNet121’s effectiveness in handling complex, high-dimensional classification tasks and noise reduction. The study underscores the model’s potential for enhancing assistive technologies and sustainable learning by providing more accurate and reliable object recognition. This advancement supports greater independence for visually impaired users and promotes more inclusive learning environments.
Keywords:Classification, Convolutional Autoencoder, Deep Learning, Images, Kitchen Utensils, Transfer Learning.
Published On: 1 April 2025
