Indoor Image Recognition and Classification via Deep Convolutional Neural Network

Afif, Mouna; Ayachi, Riadh; Said, Yahia; Pissaloux, Edwige; Atri, Mohamed

doi:10.1007/978-3-030-21005-2_35

Cited by 20 publications

(8 citation statements)

References 20 publications

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“…As mentioned in Table 3, our proposed work outperforms our previous work made in [6] and the work in [8] in term of recognition rate. We obtained very encouraging results when using MobileNet family and inception v3 architectures.…”

Section: Experiments and Resultsmentioning

confidence: 66%

“…Recognizing objects and estimating their categories and poses present a very powerful task in a wide range of applications including robotics [2], image segmentation [3], indoor object detection [4][5][6] and road sign detection [7]. This task become more challenging especially in indoor environments as they present cluttered spaces and very complex decorations.…”

Section: Introductionmentioning

confidence: 99%

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A Transfer Learning Approach for Indoor Object Identification

et al. 2021

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Accessing new indoor environments is a well-known challenge for blind and visually impaired persons (VIP). In this work, we propose a new computer vision-based indoor object recognition system used especially for indoor wayfinding and indoor assistance navigation. The proposed recognition system is based on transfer learning techniques. This system is able to detect with a big performance three categories of indoor classes (door, stairs and sign). We developed an efficient and a robust indoor landmark identification system based on a lightweight deep convolutional neural network (DCNN). The proposed detection system is generic and performant enough to handle the large intra-class variation provided in the training and the testing sets. Experimental results have shown the big efficiency of the obtained systems by achieving high recognition rates.

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Section: Experiments and Resultsmentioning

confidence: 66%

Section: Introductionmentioning

confidence: 99%

A Transfer Learning Approach for Indoor Object Identification

et al. 2021

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“…It aims to decide if an indoor sign is present in the image or not. Smart sensors as cameras present principal devices to perform indoor object detection and others applications as object detection [4.5], indoor scene recognition [6], object classification [7,8] and object segmentation [9]. Internet of things for object detection can incorporate all forms of artificial intelligence in order to facilitate life and ensure a better life for people.…”

Section: Introductionmentioning

confidence: 99%

A Ligthweight Wayfinding Assistance System for IoT Applications

Afif

Ayachi

Atri

2021

JAIBD

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In this paper, we propose to design an indoor sign detection system for industry 4.0. In order to implement the proposed system, we proposed a lightweight deep learning-based architecture based on MobileNet which can be run on embedded devices used to detect and recognize indoor landmarks signs in order to assist blind and sighted during indoor navigation. We apply various operations in order to minimize the network size as well as computation complexity. Internet of things (IoT) presents a connection between internet and the surroundings objects. IoT is characterized to connect physical objects with their numerical identities and enables them to connect with each other. This technique creates a kind of bridge between the physical world and the virtual world. The paper provides a comprehensive overview of a new method for a set of landmark indoor sign objects based on deep convolutional neural network (DCNN) for internet of things applications.

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“…Deep Learning techniques are a huge success in the field of computer vision. They have been deployed in many applications such as traffic sign detection and identification [1,2], indoor object detection and recognition [3][4][5] and many other applications [6,7]. The recognition of faces is a big challenge and an interesting research subject for different fields: psychology, model identification, computer vision, computer graphics, etc.…”

Section: Introductionmentioning

confidence: 99%

Design of a Face Recognition System based on Convolutional Neural Network (CNN)

Said

Barr

Ahmed

2020

Eng. Technol. Appl. Sci. Res.

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Face recognition is an important function of video surveillance systems, enabling verification and identification of people who appear in a scene often captured by a distributed network of cameras. The recognition of people from the faces in images arouses great interest in the scientific community, partly because of the application interests but also because of the challenge that this represents for artificial vision algorithms. They must be able to cope with the great variability of the aspects of the faces themselves as well as the variations of the shooting parameters (pose, lighting, haircut, expression, background, etc.). This paper aims to develop a face recognition application for a biometric system based on Convolutional Neural Networks. It proposes a structure of a Deep Learning model which allows improving the existing state-of-the-art precision and processing time.

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Indoor Image Recognition and Classification via Deep Convolutional Neural Network

Cited by 20 publications

References 20 publications

A Transfer Learning Approach for Indoor Object Identification

A Transfer Learning Approach for Indoor Object Identification

A Ligthweight Wayfinding Assistance System for IoT Applications

Design of a Face Recognition System based on Convolutional Neural Network (CNN)

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