Near Real-Time Object Recognition for Pepper Based on Deep Neural Networks Running on a Backpack

Reyes, Esteban; Gómez, Cristopher; Norambuena, Esteban; Ruiz‐del‐Solar, Javier

doi:10.1007/978-3-030-27544-0_24

Cited by 10 publications

(8 citation statements)

References 13 publications

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“…Reyes et al proposed a CNN method based on You Only Look Once 5 to detect object for pepper. 17 Zhu et al proposed a CNN-based indoor landmark detector with the help of a topological matching algorithm. 18 Together with classical classifier, Jiang et al proposed a CNN-based tracking method for person-following robot.…”

Section: Related Workmentioning

confidence: 99%

Geometric property-based convolutional neural network for indoor object detection

Ding

Wang

2021

International Journal of Advanced Robotic Systems

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Indoor object detection is a very demanding and important task for robot applications. Object knowledge, such as two-dimensional (2D) shape and depth information, may be helpful for detection. In this article, we focus on region-based convolutional neural network (CNN) detector and propose a geometric property-based Faster R-CNN method (GP-Faster) for indoor object detection. GP-Faster incorporates geometric property in Faster R-CNN to improve the detection performance. In detail, we first use mesh grids that are the intersections of direct and inverse proportion functions to generate appropriate anchors for indoor objects. After the anchors are regressed to the regions of interest produced by a region proposal network (RPN-RoIs), we then use 2D geometric constraints to refine the RPN-RoIs, in which the 2D constraint of every classification is a convex hull region enclosing the width and height coordinates of the ground-truth boxes on the training set. Comparison experiments are implemented on two indoor datasets SUN2012 and NYUv2. Since the depth information is available in NYUv2, we involve depth constraints in GP-Faster and propose 3D geometric property-based Faster R-CNN (DGP-Faster) on NYUv2. The experimental results show that both GP-Faster and DGP-Faster increase the performance of the mean average precision.

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Section: Related Workmentioning

confidence: 99%

Geometric property-based convolutional neural network for indoor object detection

Ding

Wang

2021

International Journal of Advanced Robotic Systems

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“…The annotated text on the bounding box can be translated into voice response and the fundamental positions of the objects can be provided from the perspective of the person's location. By recognizing the front objects and making them aware of the danger, the object sensing module in the system will help the blind to also provide a safe route to reach the destination [3].…”

Section: Amentioning

confidence: 99%

“…YOLO's very quick. We simply run our CNN on a picture in order to forecast detections [3]. You Only Look Once (YOLO) [10], the computer vision device capable of detecting a range of objects in one image, with an accuracy similiar to RetinaNet, but with higher inferiority than with other existing systems, such as SS [11], R-FCN [12] and FPN FRCN [13].…”

Section: 11mentioning

confidence: 99%

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Electronic Orientation Aid for Visually Impaired using Graphics Processing Unit (GPU)

et al. 2020

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Vision is one of the very essential human senses and it plays a major role in human perception about surrounding environment. But for people with visual impairment their definition of vision is different. Visually impaired people are often unaware of dangers in front of them, even in familiar environment. This study proposes a real time guiding system for visually impaired people for solving their navigation problem and to travel without any difficulty. This system will help the visually impaired people by detecting the objects and giving necessary information about that object. This information may include what the object is, its location, its precision, distance from the visually impaired etc. All these information will be conveyed to the person through audio commands so that they can navigate freely anywhere anytime with no or minimal assistance. Object detection is done using You Only Look Once (YOLO) algorithm. As the process of capturing the video/images and sending it to the main module has to be carried at greater speed, Graphics Processing Unit (GPU) is used. This will help in enhancing the overall speed of the system and will help the visually Impaired to get the maximum necessary instructions as quickly as possible. The process starts from capturing the real time video, sending it for analysis and processing and get the calculated results. The results obtained from analysis are conveyed to user by means of hearing aid. As a result by this system the blind or the visually impaired people can visualize the surrounding environment and travel freely from source to destination on their own.

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“…The first case is presented by state-of-the-art methods for object recognition, such as the currently fastest object detector, YOLOv3 (Redmon and Farhadi, 2018 ). Deep Learning methods can achieve a high performance but require the support of powerful GPUs which are usually not available on the type of robot systems we are targeting (see Reyes et al, 2018 ). The second class of methods can be found in more specific areas.…”

Section: Introductionmentioning

confidence: 99%

On the Illumination Influence for Object Learning on Robot Companions

Keller

Lohan

2020

Front. Robot. AI

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Most collaborative tasks require interaction with everyday objects (e.g., utensils while cooking). Thus, robots must perceive everyday objects in an effective and efficient way. This highlights the necessity of understanding environmental factors and their impact on visual perception, such as illumination changes throughout the day on robotic systems in the real world. In object recognition, two of these factors are changes due to illumination of the scene and differences in the sensors capturing it. In this paper, we will present data augmentations for object recognition that enhance a deep learning architecture. We will show how simple linear and non-linear illumination models and feature concatenation can be used to improve deep learning-based approaches. The aim of this work is to allow for more realistic Human-Robot Interaction scenarios with a small amount of training data in combination with incremental interactive object learning. This will benefit the interaction with the robot to maximize object learning for long-term and location-independent learning in unshaped environments. With our model-based analysis, we showed that changes in illumination affect recognition approaches that use Deep Convolutional Neural Network to encode features for object recognition. Using data augmentation, we were able to show that such a system can be modified toward a more robust recognition without retraining the network. Additionally, we have shown that using simple brightness change models can help to improve the recognition across all training set sizes.

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Near Real-Time Object Recognition for Pepper Based on Deep Neural Networks Running on a Backpack

Cited by 10 publications

References 13 publications

Geometric property-based convolutional neural network for indoor object detection

Geometric property-based convolutional neural network for indoor object detection

Electronic Orientation Aid for Visually Impaired using Graphics Processing Unit (GPU)

On the Illumination Influence for Object Learning on Robot Companions

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