Título: On-device Deep Learning for Recognizing 3D Geometric Shapes in an Educational App Using the TinyML Paradigm
Autores: André de Jesus Araújo Ramos, Roberto Célio Limão de Oliveira & Manoel Freitas Campos Neto
Resumo: Currently, deep learning (DL) algorithms perform best in image classification and object detection tasks. Consequently, they are frequently used to address most problems involving computer vision. In this sense, the pervasive presence of smartphones and IoT devices has created a need to make this artificial intelligence portable. Given that deep neural network (DNN) models consist of millions of parameters, emerging research efforts have focused on enabling offline DL execution on low-resource devices, such as within the TinyML paradigm. This study analyzes the state-of-the-art of DL embedded in smartphones to develop an app for children that can recognize 3D geometric shapes without needing an internet connection. Alongside a systematic literature review, we conduct experiments with several pre-trained and lightweight models, which were subsequently evaluated using parametric statistical tests. While DL on smartphones is an underexplored area, it is expected to evolve significantly. Among the classification models tested, DenseNet169 demonstrated the highest accuracy (81%), whereas the MobileNet variants were faster and closer to real-time performance (30 FPS). In detection tasks, the EfficientDet-Lite and YOLOv8 models were evaluated, with EfficientDet-Lite being less accurate but faster (50 ms) compared to YOLOv8 (4 seconds). Although the field of DL on smartphones still requires further development, current lightweight models and frameworks offer significant opportunities for practical application.
Palavras-chave: computer vision; deep learning; education; tinyml.
Páginas: 8
Código DOI: 10.21528/CBIC2025-1175612
Artigo em PDF: CBIC_2025_paper1175612.pdf
Arquivo BibTeX:
CBIC_2025_1175612.bib