Dual-Path Autoencoder-DenseNet Architecture for Robust Object Classification via Background-Free Reconstruction and Multi-Task Feature Fusion

Object classification in real-world environments is often challenged by image degradation such as noise, blur, and poor lighting, which degrades the performance of conventional deep learning models. To address this, we propose a multi-task dual-path autoencoder-DenseNet framework that simultaneously...

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Vydané v:	2025 International Conference on Artificial Intelligence and Digital Ethics (ICAIDE) s. 458 - 463
Hlavní autori:	Rosli, Hashim, Ali, Rozniza, Hitam, Muhamad Suzuri, Deris, Ashanira Mat
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 29.05.2025
Predmet:	autoencoder Autoencoders component Feature extraction Image reconstruction loss function Mean square error methods multi-task Multitasking Noise object classification Pipelines Root mean square Service robots Visualization
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