Communication Dans Un Congrès Année : 2024

Real-Time Indoor Object Detection Based on Hybrid CNN-Transformer Approach

Résumé

Real-time object detection in indoor settings is a challenging area of computer vision, faced with unique obstacles such as variable lighting and complex backgrounds. This field holds significant potential to revolutionize applications like augmented and mixed realities by enabling more seamless interactions between digital content and the physical world. However, the scarcity of research specifically fitted to the intricacies of indoor environments has highlighted a clear gap in the literature. To address this, our study delves into the evaluation of existing datasets and computational models, leading to the creation of a refined dataset. This new dataset is derived from OpenImages v7[14], focusing exclusively on 32 indoor categories selected for their relevance to real-world applications. Alongside this, we present an adaptation of a CNN detection model, incorporating an attention mechanism to enhance the model’s ability to discern and prioritize critical features within cluttered indoor scenes. Our findings demonstrate that this approach is not just competitive to existing state-of-the-art models in accuracy and speed but also opens new avenues for research and application in the field of real-time indoor object detection.
Fichier non déposé

Dates et versions

hal-04864671 , version 1 (05-01-2025)

Identifiants

Citer

Salah-Eddine Laidoudi, Madjid Maidi, Samir Otmane. Real-Time Indoor Object Detection Based on Hybrid CNN-Transformer Approach. 27th European Conference on Artificial Intelligence (ECAI 2024), Oct 2024, Santiago de Compostela, Spain. pp.459--466, ⟨10.3233/FAIA240521⟩. ⟨hal-04864671⟩
0 Consultations
0 Téléchargements

Altmetric

Partager

More