A robust multiclass 3D object recognition based on modern YOLO deep learning algorithms

2021 ◽  
Author(s):  
Mariam L. Francies ◽  
Mohamed M. Ata ◽  
Mohamed A. Mohamed
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Learning Algorithms ◽  
3D Object Recognition ◽  
3D Object

Review of Multi-view 3D Object Recognition Methods Based on Deep Learning

Displays ◽  
2021 ◽  
pp. 102053
Author(s):  
Shaohua Qi ◽  
Xin Ning ◽  
Guowei Yang ◽  
Liping Zhang ◽  
Peng Long ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
3D Object Recognition ◽  
3D Object

scholarly journals Par3DNet: Using 3DCNNs for Object Recognition on Tridimensional Partial Views

2020 ◽  
Vol 10 (10) ◽  
pp. 3409
Author(s):  
Francisco Gomez-Donoso ◽  
Felix Escalona ◽  
Miguel Cazorla
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Point Cloud ◽  
Real Data ◽  
3D Object Recognition ◽  
3D Object ◽  
3D Data

Deep learning-based methods have proven to be the best performers when it comes to object recognition cues both in images and tridimensional data. Nonetheless, when it comes to 3D object recognition, the authors tend to convert the 3D data to images and then perform their classification. However, despite its accuracy, this approach has some issues. In this work, we present a deep learning pipeline for object recognition that takes a point cloud as input and provides the classification probabilities as output. Our proposal is trained on synthetic CAD objects and is able to perform accurately when fed with real data provided by commercial sensors. Unlike most approaches, our method is specifically trained to work on partial views of the objects rather than on a full representation, which is not the representation of the objects as captured by commercial sensors. We trained our proposal with the ModelNet10 dataset and achieved a 78.39 % accuracy. We also tested it by adding noise to the dataset and against a number of datasets and real data with high success.

scholarly journals Processing chain for 3D histogram of gradients based real-time object recognition

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142097836
Author(s):  
Cristian Vilar ◽  
Silvia Krug ◽  
Benny Thörnberg
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Real Time ◽  
Recognition Accuracy ◽  
3D Object Recognition ◽  
Learning Approaches ◽  
Data Set ◽  
3D Object ◽  
Time Performance ◽  
Voxel Grid

3D object recognition has been a cutting-edge research topic since the popularization of depth cameras. These cameras enhance the perception of the environment and so are particularly suitable for autonomous robot navigation applications. Advanced deep learning approaches for 3D object recognition are based on complex algorithms and demand powerful hardware resources. However, autonomous robots and powered wheelchairs have limited resources, which affects the implementation of these algorithms for real-time performance. We propose to use instead a 3D voxel-based extension of the 2D histogram of oriented gradients (3DVHOG) as a handcrafted object descriptor for 3D object recognition in combination with a pose normalization method for rotational invariance and a supervised object classifier. The experimental goal is to reduce the overall complexity and the system hardware requirements, and thus enable a feasible real-time hardware implementation. This article compares the 3DVHOG object recognition rates with those of other 3D recognition approaches, using the ModelNet10 object data set as a reference. We analyze the recognition accuracy for 3DVHOG using a variety of voxel grid selections, different numbers of neurons ( Nh) in the single hidden layer feedforward neural network, and feature dimensionality reduction using principal component analysis. The experimental results show that the 3DVHOG descriptor achieves a recognition accuracy of 84.91% with a total processing time of 21.4 ms. Despite the lower recognition accuracy, this is close to the current state-of-the-art approaches for deep learning while enabling real-time performance.

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