Summary of Real-time Indoor Object Detection Based on Hybrid Cnn-transformer Approach, by Salah Eddine Laidoudi et al.
Real-Time Indoor Object Detection based on hybrid CNN-Transformer Approach
by Salah Eddine Laidoudi, Madjid Maidi, Samir Otmane
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses the challenge of real-time object detection in indoor settings, which is crucial for applications like augmented and mixed realities. The scarcity of research focused on indoor environments highlights a gap in the literature. To address this, the study evaluates existing datasets and computational models, leading to the creation of a refined dataset derived from OpenImages v7, focusing on 32 indoor categories relevant to real-world applications. Additionally, an adaptation of a CNN detection model is presented, incorporating an attention mechanism to enhance feature prioritization in cluttered indoor scenes. The findings demonstrate that this approach is competitive with existing state-of-the-art models in terms of accuracy and speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Real-time object detection in indoor settings is important because it can make interactions between digital content and the physical world more seamless. However, there’s a lack of research on this topic, which means we don’t have good ways to do it yet. The study creates a new dataset that focuses only on 32 types of objects found indoors, like furniture or decorations. It also develops a new way for computers to detect these objects using a type of artificial intelligence called convolutional neural networks (CNNs). This approach is not only good at detecting objects but also fast and efficient. |
Keywords
» Artificial intelligence » Attention » Cnn » Object detection
