A Transfer Learning Approach for Indoor Object Identification

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.

[1]  Partha Pratim Roy,et al.  Quadbox: Quadrilateral Bounding Box Based Scene Text Detection Using Vector Regression , 2021, IEEE Access.

[2]  Mohamed Atri,et al.  Indoor Image Recognition and Classification via Deep Convolutional Neural Network , 2018, Smart Innovation, Systems and Technologies.

[3]  Nuwan Madusanka,et al.  A Comparative Study of Alzheimer's Disease Classification using Multiple Transfer Learning Models , 2019, J. Multim. Inf. Syst..

[4]  Place Classification Algorithm Based on Semantic Segmented Objects , 2020, Applied Sciences.

[5]  Riadh Ayachi,et al.  An Evaluation of RetinaNet on Indoor Object Detection for Blind and Visually Impaired Persons Assistance Navigation , 2020, Neural Processing Letters.

[6]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Mohamed Atri,et al.  Indoor Object C1assification for Autonomous Navigation Assistance Based on Deep CNN Model , 2019, 2019 IEEE International Symposium on Measurements & Networking (M&N).

[8]  Mohamed Atri,et al.  Traffic Signs Detection for Real-World Application of an Advanced Driving Assisting System Using Deep Learning , 2019, Neural Processing Letters.

[9]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[10]  Xindong Wu,et al.  Object Detection With Deep Learning: A Review , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[11]  Francesc Moreno-Noguer,et al.  Learning RGB-D descriptors of garment parts for informed robot grasping , 2014, Eng. Appl. Artif. Intell..

[12]  Dumitru Erhan,et al.  Deep Neural Networks for Object Detection , 2013, NIPS.

[13]  Deep Learning based Approach for Bone Diagnosis Classification in Ultrasonic Computed Tomographic Images , 2020 .

[14]  Debi Prosad Dogra,et al.  deepGesture: Deep learning-based gesture recognition scheme using motion sensors , 2018, Displays.

[15]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[16]  Byung-Gyu Kim,et al.  Deep Joint Spatiotemporal Network (DJSTN) for Efficient Facial Expression Recognition , 2020, Sensors.

[17]  Hua Yang,et al.  Discriminative feature representation for image classification via multimodal multitask deep neural networks , 2017, J. Electronic Imaging.

[18]  Derek Nowrouzezahrai,et al.  Learning hatching for pen-and-ink illustration of surfaces , 2012, TOGS.

[19]  Paramartha Dutta,et al.  Advancements in Image Classification using Convolutional Neural Network , 2018, 2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN).

[20]  Stephen Gould,et al.  Discriminative learning with latent variables for cluttered indoor scene understanding , 2010, CACM.

[21]  Geoffrey A. Hollinger,et al.  HERB: a home exploring robotic butler , 2010, Auton. Robots.

[22]  Ramón Barber,et al.  Object Detection Applied to Indoor Environments for Mobile Robot Navigation , 2016, Sensors.

[23]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Ke Xie,et al.  A search-classify approach for cluttered indoor scene understanding , 2012, ACM Trans. Graph..

[25]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

[26]  Mark Sandler,et al.  MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[27]  Riadh Ayachi,et al.  Deep Learning Based Application for Indoor Scene Recognition , 2020, Neural Processing Letters.

[28]  Marc'Aurelio Ranzato,et al.  Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[29]  Vikas Sharma,et al.  Detection of Rice Disease Using Bayes' Classifier and Minimum Distance Classifier , 2020, J. Multim. Inf. Syst..

[30]  Faouzi Benzarti,et al.  A new method for text detection and recognition in indoor scene for assisting blind people , 2017, International Conference on Machine Vision.

[31]  Abien Fred Agarap.  Deep Learning using Rectified Linear Units (ReLU) , 2018, ArXiv.

[32]  Angel P. del Pobil,et al.  Object Detection and Recognition for Assistive Robots: Experimentation and Implementation , 2017, IEEE Robotics & Automation Magazine.

[33]  Shaogang Gong,et al.  Deep Learning Logo Detection with Data Expansion by Synthesising Context , 2016, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).

[34]  Eric R. LaRose,et al.  MCIndoor20000: A fully-labeled image dataset to advance indoor objects detection , 2018, Data in brief.