Multi-source data fusion using deep learning for smart refrigerators

Zhang, Weishan; Zhang, Yuanjie; Zhai, Jia; Zhao, Dehai; Xu, Liang; Zhou, Jiehan; Li, Zhongwei; Yang, Su

doi:10.1016/j.compind.2017.09.001

Cited by 54 publications

(21 citation statements)

References 6 publications

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“…Zhang et al. () integrated AI technology into the design of smart refrigerator to achieve fruit recognition. The proposed approach focused on distinguishing different kinds of fruits and identifying different individuals in the image.…”

Section: Quality Detection Of Fruitsmentioning

confidence: 99%

“…Multisource data fusion have not been fully utilized in food quality and safety evaluation with deep learning. One example was given in this paper that considered the combination of image and weight information of fruits to give a more accurate identification of fruits (Zhang et al., ). Multisource data (or features) fusion based on more types of data from advanced sensors could be further used to achieve a more comprehensive and accurate evaluation of the food.…”

Section: Challenges and Future Perspective Of Deep Learning In Food Dmentioning

confidence: 99%

“…Zhang et al. () considered to assign computing tasks to a cloud server. The captured picture would be transmitted to the remote server for analysis and the result would return to the APP.…”

Section: Challenges and Future Perspective Of Deep Learning In Food Dmentioning

confidence: 99%

See 2 more Smart Citations

Application of Deep Learning in Food: A Review

Zhou

Zhang

Liu

et al. 2019

Comp Rev Food Sci Food Safe

388

166

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Deep learning has been proved to be an advanced technology for big data analysis with a large number of successful cases in image processing, speech recognition, object detection, and so on. Recently, it has also been introduced in food science and engineering. To our knowledge, this review is the first in the food domain. In this paper, we provided a brief introduction of deep learning and detailedly described the structure of some popular architectures of deep neural networks and the approaches for training a model. We surveyed dozens of articles that used deep learning as the data analysis tool to solve the problems and challenges in food domain, including food recognition, calories estimation, quality detection of fruits, vegetables, meat and aquatic products, food supply chain, and food contamination. The specific problems, the datasets, the preprocessing methods, the networks and frameworks used, the performance achieved, and the comparison with other popular solutions of each research were investigated. We also analyzed the potential of deep learning to be used as an advanced data mining tool in food sensory and consume researches. The result of our survey indicates that deep learning outperforms other methods such as manual feature extractors, conventional machine learning algorithms, and deep learning as a promising tool in food quality and safety inspection. The encouraging results in classification and regression problems achieved by deep learning will attract more research efforts to apply deep learning into the field of food in the future.

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Section: Quality Detection Of Fruitsmentioning

confidence: 99%

Section: Challenges and Future Perspective Of Deep Learning In Food Dmentioning

confidence: 99%

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Application of Deep Learning in Food: A Review

Zhou

Zhang

Liu

et al. 2019

Comp Rev Food Sci Food Safe

388

166

View full text Add to dashboard Cite

show abstract

“…It has been running stably in the past year, which shows the effectiveness of our solution. In the future, we will adopt a multi-model data fusion approach [23,24] to improve the recognition accuracy.…”

Section: Discussionmentioning

confidence: 99%

Embedded Deep Learning for Ship Detection and Recognition

2019

Self Cite

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Ship detection and recognition are important for smart monitoring of ships in order to manage port resources effectively. However, this is challenging due to complex ship profiles, ship background, object occlusion, variations of weather and light conditions, and other issues. It is also expensive to transmit monitoring video in a whole, especially if the port is not in a rural area. In this paper, we propose an on-site processing approach, which is called Embedded Ship Detection and Recognition using Deep Learning (ESDR-DL). In ESDR-DL, the video stream is processed using embedded devices, and we design a two-stage neural network named DCNet, which is composed of a DNet for ship detection and a CNet for ship recognition, running on embedded devices. We have extensively evaluated ESDR-DL, including performance of accuracy and efficiency. The ESDR-DL is deployed at the Dongying port of China, which has been running for over a year and demonstrates that it can work reliably for practical usage.

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“…Many of the recent research models are working for object detection from inside a refrigerator. The model proposed in [44] works for detecting fruits inside a refrigerator to make refrigerator smart. Smart refrigerator with fruit detection capability can help us detecting and recognizing fruits, number of every fruit and even freshness or lameness of fruit.…”

Section: Case 3 -Input: Image From Inside a Refrigeratormentioning

confidence: 99%

Multi Class Fruit Classification Using Efficient Object Detection and Recognition Techniques

Khan¹,

Debnath²

2019

IJIGSP

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In this paper, an efficient approach has been proposed to localize every clearly visible object or region of object from an image, using less memory and computing power. For object detection we have processed every input image to overcome several complexities, which are the main limitations to achieve better result, such as overlap between multiple objects, noise in the image background, poor resolution etc. We have also implemented an improved Convolutional Neural Network based classification or recognition algorithm which has proved to provide better performance than baseline works. Combining these two detection and recognition approaches, we have developed a competent multi-class Fruit Detection and Recognition (FDR) model that is very proficient regardless of different limitations such as high and poor image quality, complex background or lightening condition, different fruits of same shape and color, multiple overlapped fruits, existence of non-fruit object in the image and the variety in size, shape, angel and feature of fruit. This proposed FDR model is also capable of detecting every single fruit separately from a set of overlapping fruits. Another major contribution of our FDR model is that it is not a dataset oriented model which works better on only a particular dataset as it has been proved to provide better performance while applying on both real world images (e.g., our own dataset) and several states of art datasets. Nevertheless, taking a number of challenges into consideration, our proposed model is capable of detecting and recognizing fruits from image with a better accuracy and average precision rate of about 0.9875.

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Multi-source data fusion using deep learning for smart refrigerators

Cited by 54 publications

References 6 publications

Application of Deep Learning in Food: A Review

Application of Deep Learning in Food: A Review

Embedded Deep Learning for Ship Detection and Recognition

Multi Class Fruit Classification Using Efficient Object Detection and Recognition Techniques

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