A Deep Learning Approach for Product Detection in Intelligent Retail Environment

2021 ◽  
Author(s):  
Giulia Pazzaglia ◽  
Marco Mameli ◽  
Emanuele Frontoni ◽  
Primo Zingaretti ◽  
Rocco Pietrini ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Customer Satisfaction ◽  
Graphical Representation ◽  
Low Cost ◽  
Image Acquisition ◽  
Experimental Results ◽  
Learning Approach ◽  
Retail Environment ◽  
Real Environment

Abstract A planogram is the graphical representation of the way a given number of products are positioned within the shelves in a store. The creation of a correct planogram is a fundamental tool for a store’s performance: it helps to increase sales and achieve maximum customer satisfaction by reducing out-of-stocks. To this end, this work aims to provide an automatic object recognition based system that allows the operator to verify the correctness of a planogram. For image acquisition, either low-cost battery-powered cameras positioned on the opposite side of the shelf or simply a tablet with a dedicated app can be used. These tools are connected to the cloud where the detection and matching phases are performed. The experimental results come from a real environment.

scholarly journals Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 982 ◽  
Author(s):  
Hyo Lee ◽  
Ihsan Ullah ◽  
Weiguo Wan ◽  
Yongbin Gao ◽  
Zhijun Fang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Large Scale ◽  
Recognition Rate ◽  
Experimental Results ◽  
Learning Approach ◽  
Deep Model ◽  
Proposed Model ◽  
Real Time Applications ◽  
Model Recognition

Make and model recognition (MMR) of vehicles plays an important role in automatic vision-based systems. This paper proposes a novel deep learning approach for MMR using the SqueezeNet architecture. The frontal views of vehicle images are first extracted and fed into a deep network for training and testing. The SqueezeNet architecture with bypass connections between the Fire modules, a variant of the vanilla SqueezeNet, is employed for this study, which makes our MMR system more efficient. The experimental results on our collected large-scale vehicle datasets indicate that the proposed model achieves 96.3% recognition rate at the rank-1 level with an economical time slice of 108.8 ms. For inference tasks, the deployed deep model requires less than 5 MB of space and thus has a great viability in real-time applications.

scholarly journals A Deep Learning Approach for Object Recognition with NAO Soccer Robots

2017 ◽  
pp. 392-403 ◽  
Author(s):  
Dario Albani ◽  
Ali Youssef ◽  
Vincenzo Suriani ◽  
Daniele Nardi ◽  
Domenico Daniele Bloisi
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Learning Approach ◽  
Soccer Robots

scholarly journals Automated In Situ Seed Variety Identification via Deep Learning: A Case Study in Chickpea

Plants ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1406
Author(s):  
Amin Taheri-Garavand ◽  
Amin Nasiri ◽  
Dimitrios Fanourakis ◽  
Soodabeh Fatahi ◽  
Mahmoud Omid ◽  
...  
Keyword(s):  
Deep Learning ◽  
Crop Production ◽  
Low Cost ◽  
Image Acquisition ◽  
Visible Spectrum ◽  
Variety Identification ◽  
Automatic Identification ◽  
Seed Industry ◽  
Novel Applications

On-time seed variety recognition is critical to limit qualitative and quantitative yield loss and asynchronous crop production. The conventional method is a subjective and error-prone process, since it relies on human experts and usually requires accredited seed material. This paper presents a convolutional neural network (CNN) framework for automatic identification of chickpea varieties by using seed images in the visible spectrum (400–700 nm). Two low-cost devices were employed for image acquisition. Lighting and imaging (background, focus, angle, and camera-to-sample distance) conditions were variable. The VGG16 architecture was modified by a global average pooling layer, dense layers, a batch normalization layer, and a dropout layer. Distinguishing the intricate visual features of the diverse chickpea varieties and recognizing them according to these features was conceivable by the obtained model. A five-fold cross-validation was performed to evaluate the uncertainty and predictive efficiency of the CNN model. The modified deep learning model was able to recognize different chickpea seed varieties with an average classification accuracy of over 94%. In addition, the proposed vision-based model was very robust in seed variety identification, and independent of image acquisition device, light environment, and imaging settings. This opens the avenue for the extension into novel applications using mobile phones to acquire and process information in situ. The proposed procedure derives possibilities for deployment in the seed industry and mobile applications for fast and robust automated seed identification practices.

scholarly journals A Deep Learning Approach to Nightfire Detection based on Low-Light Satellite

2021 ◽  
Author(s):  
Yue Wang ◽  
Ye Ni ◽  
Xutao Li ◽  
Yunming Ye
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Early Detection ◽  
Remote Sensing Image ◽  
Experimental Results ◽  
Learning Approach ◽  
Learning Approaches ◽  
Low Light ◽  
Conventional Machine

Wildfires are a serious disaster, which often cause severe damages to forests and plants. Without an early detection and suitable control action, a small wildfire could grow into a big and serious one. The problem is especially fatal at night, as firefighters in general miss the chance to detect the wildfires in the very first few hours. Low-light satellites, which take pictures at night, offer an opportunity to detect night fire timely. However, previous studies identify night fires based on threshold methods or conventional machine learning approaches, which are not robust and accurate enough. In this paper, we develop a new deep learning approach, which determines night fire locations by a pixel-level classification on low-light remote sensing image. Experimental results on VIIRS data demonstrate the superiority and effectiveness of the proposed method, which outperforms conventional threshold and machine learning approaches.

scholarly journals A Multi-Task Approach to Open Domain Suggestion Mining (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13817-13818
Author(s):  
Minni Jain ◽  
Maitree Leekha ◽  
Mononito Goswami
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Class Imbalance ◽  
Sampling Technique ◽  
Experimental Results ◽  
Single Domain ◽  
Learning Approach ◽  
Open Domain ◽  
Consumer Reviews ◽  
Multiple Domains

Consumer reviews online may contain suggestions useful for improving the target products and services. Mining suggestions is challenging because the field lacks large labelled and balanced datasets. Furthermore, most prior studies have only focused on mining suggestions in a single domain. In this work, we introduce a novel up-sampling technique to address the problem of class imbalance, and propose a multi-task deep learning approach for mining suggestions from multiple domains. Experimental results on a publicly available dataset show that our up-sampling technique coupled with the multi-task framework outperforms state-of-the-art open domain suggestion mining models in terms of the F-1 measure and AUC.

scholarly journals Sentiment Analysis in Spanish for Improvement of Products and Services: A Deep Learning Approach

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Mario Andrés Paredes-Valverde ◽  
Ricardo Colomo-Palacios ◽  
María del Pilar Salas-Zárate ◽  
Rafael Valencia-García
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Deep Learning ◽  
Public Opinion ◽  
Customer Satisfaction ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Learning Approach ◽  
Complete Dataset

Sentiment analysis is an important area that allows knowing public opinion of the users about several aspects. This information helps organizations to know customer satisfaction. Social networks such as Twitter are important information channels because information in real time can be obtained and processed from them. In this sense, we propose a deep-learning-based approach that allows companies and organizations to detect opportunities for improving the quality of their products or services through sentiment analysis. This approach is based on convolutional neural network (CNN) and word2vec. To determine the effectiveness of this approach for classifying tweets, we conducted experiments with different sizes of a Twitter corpus composed of 100000 tweets. We obtained encouraging results with a precision of 88.7%, a recall of 88.7%, and an F-measure of 88.7% considering the complete dataset.

A Deep Learning Approach to MRI Scanner Manufacturer and Model Identification

2020 ◽  
Vol 2020 (4) ◽  
pp. 217-1-217-7
Author(s):  
Shengbang Fang ◽  
Ronnie A. Sebro ◽  
Matthew C. Stamm
Keyword(s):  
Deep Learning ◽  
Digital Image ◽  
Source Identification ◽  
Medical Records ◽  
Model Identification ◽  
Diagnostic Techniques ◽  
Experimental Results ◽  
Learning Approach ◽  
Scientific Fraud ◽  
Mri Image

Forensics research has developed several techniques to identify the model and manufacturer of a digital image or videos source camera. However, to the best of our knowledge, no work has been performed to identify the manufacturer and model of the scanner that captured an MRI image. MRI source identification can have several important applications ranging from scientific fraud discovery, exposing issues around anonymity and privacy of medical records, protecting against malicious tampering of medical images, and validating AI-based diagnostic techniques whose performance varies on different MRI scanners. In this paper, we propose a new CNN-based approach to learn forensic traces left by an MRI scanner and use these traces to identify the manufacturer and model of the scanner that captured an MRI image. Additionally, we identify an issue called weight divergence that can occur when training CNNs using a constrained convolutional layer and propose three new correction functions to protect against this. Our experimental results show we can identify an MRI scanners manufacturer with 97.88% accuracy and its model with 91.07% accuracy. Additionally, we show that our proposed correction functions can noticeably improve our CNNs accuracy when performing scanner model identification.

Deep Learning Early Stopping for Non-degenerate Ghost Imaging

2021 ◽  
Author(s):  
Chane Moodley ◽  
Bereneice Sephton ◽  
Valeria Rodríguez-Fajardo ◽  
Andrew Forbes
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Acquisition Time ◽  
Physical Parameters ◽  
Learning Approach ◽  
Ghost Imaging ◽  
Early Stopping ◽  
Mask Type ◽  
Image Acquisition Time ◽  
Imaging Setup

Abstract Quantum ghost imaging offers many advantages over classical imaging, including the ability to probe an object with one wavelength and record the image with another (non-degenerate ghost imaging), but suffers from slow image reconstruction due to sparsity and probabilistic arrival positions of photons. Here, we propose a two-step deep learning approach to establish an optimal early stopping point based on object recognition, even for sparsely filled images. In step one we enhance the reconstructed image after every measurement by a deep convolutional auto encoder, followed by step two in which a classifier is used to recognise the image. We test this approach on a non degenerate ghost imaging setup while varying physical parameters such as the mask type and resolution. We achieved a 5-fold decrease in image acquisition time at a recognition confidence of 75%. The significant reduction in experimental running time is an important step towards real-time ghost imaging, as well as object recognition with few photons, e.g., in the detection of light sensitive structures.

scholarly journals Deep learning early stopping for non-degenerate ghost imaging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chané Moodley ◽  
Bereneice Sephton ◽  
Valeria Rodríguez-Fajardo ◽  
Andrew Forbes
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Acquisition Time ◽  
Physical Parameters ◽  
Learning Approach ◽  
Ghost Imaging ◽  
Early Stopping ◽  
Mask Type ◽  
Image Acquisition Time ◽  
Imaging Setup

AbstractQuantum ghost imaging offers many advantages over classical imaging, including the ability to probe an object with one wavelength and record the image with another (non-degenerate ghost imaging), but suffers from slow image reconstruction due to sparsity and probabilistic arrival positions of photons. Here, we propose a two-step deep learning approach to establish an optimal early stopping point based on object recognition, even for sparsely filled images. In step one we enhance the reconstructed image after every measurement by a deep convolutional auto-encoder, followed by step two in which a classifier is used to recognise the image. We test this approach on a non-degenerate ghost imaging setup while varying physical parameters such as the mask type and resolution. We achieved a fivefold decrease in image acquisition time at a recognition confidence of $$75\%$$ 75 % . The significant reduction in experimental running time is an important step towards real-time ghost imaging, as well as object recognition with few photons, e.g., in the detection of light sensitive structures.

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