Food Image Classification with Improved MobileNet Architecture and Data Augmentation

Phiphiphatphaisit, Sirawan; Surinta, Olarik

doi:10.1145/3388176.3388179

Cited by 44 publications

(13 citation statements)

References 13 publications

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“…The first successful application of CNNs as a deep learning application to images was shaped by LeCun, Bengio and Haffner in 1998 [ 41 ], but only in 2012 with the new architecture of CNN, AlexNet, did the statistical results for the image classification task really advance the state of the art [ 42 ]. Thereafter, progress on exploring computer vision has driven advances in the analysis of food images [ 43 ]. In this context, deep learning architectures such as MobileNetV2 combined with X-ray images can provide rapid predictions and recommendations for the next steps of the maize grain quality evaluation.…”

Section: Discussionmentioning

confidence: 99%

Convolutional Neural Networks Using Enhanced Radiographs for Real-Time Detection of Sitophilus zeamais in Maize Grain

Silva

Barroso

et al. 2021

Foods

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The application of artificial intelligence (AI) such as deep learning in the quality control of grains has the potential to assist analysts in decision making and improving procedures. Advanced technologies based on X-ray imaging provide markedly easier ways to control insect infestation of stored products, regardless of whether the quality features are visible on the surface of the grains. Here, we applied contrast enhancement algorithms based on peripheral equalization and calcification emphasis on X-ray images to improve the detection of Sitophilus zeamais in maize grains. In addition, we proposed an approach based on convolutional neural networks (CNNs) to identity non-infested and infested classes using three different architectures; (i) Inception-ResNet-v2, (ii) Xception and (iii) MobileNetV2. In general, the prediction models developed based on the MobileNetV2 and Xception architectures achieved higher accuracy (≥0.88) in identifying non-infested grains and grains infested by maize weevil, with a correct classification from 0.78 to 1.00 for validation and test sets. Hence, the proposed approach using enhanced radiographs has the potential to provide precise control of Sitophilus zeamais for safe human consumption of maize grains. The proposed method can automatically recognize food contaminated with hidden storage pests without manual features, which makes it more reliable for grain inspection.

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Section: Discussionmentioning

confidence: 99%

Convolutional Neural Networks Using Enhanced Radiographs for Real-Time Detection of Sitophilus zeamais in Maize Grain

Silva

Barroso

et al. 2021

Foods

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“…In other words, first a spatial convolution is performed independently for each channel, then by a 1x1 convolution across all channels. This approach was found to be easier than the normal 3D convolution [52].…”

Section: ) Squeezenetmentioning

confidence: 98%

Low-Cost CNN for Automatic Violence Recognition on Embedded System

et al. 2022

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Due to the increasing number of violence cases, there is a high demand for efficient monitoring systems, however, these systems can be susceptible to failure. Therefore, this work proposes the analysis and application of low-cost Convolutional Neural Networks (CNNs) techniques to automatically recognize and classify suspicious events. Thus, it is possible to alert and assist the monitoring process with a reduced deployment cost. For this purpose, a dataset with violence and non-violence actions in scenes of crowded and non-crowded environments was assembled. The mobile CNNs architectures were adapted and obtained a classification accuracy of up to 92.05%, with a low number of parameters. To demonstrate the models' validity, a prototype was developed by using an embedded Raspberry Pi platform, able to execute a model in real-time with 4 frames-per-second of speed. In addition, a warning system was developed to recognize pre-fight behavior and anticipate violent acts, alerting security to potential situations.

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“…In the case of image recognition, the recognition model always benefits from a large amount of image data [5]. Data augmentation procedures used for expanding data set, such as translation in [6], [7] while histogram equalization is used to enhance the x-ray image [8]. Mikołajczyk and Grochowski [9] studied and compared numerous approaches of data augmentation for image classification in order to progress the training process proficiency.…”

Section: Introductionmentioning

confidence: 99%

Expansion dataset COVID-19 chest X-ray using data augmentation and histogram equalization

Alkhalid

Albayati

Alhammad

2022

IJECE

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The main important factor that plays vital role in success the deep learning is the deep training by many and many images, if neural networks are getting bigger and bigger but the training datasets are not, then it sounds like going to hit an accuracy wall. Briefly, this paper investigates the current state of the art of approaches used for a data augmentation for expansion the corona virus disease 2019 (COVID-19) chest X-ray images using different data augmentation methods (transformation and enhancement) the dataset expansion helps to rise numbers of images from 138 to 5520, the increasing rate is 3,900%, this proposed model can be used to expand any type of image dataset, in addition, the dataset have used with convolutional neural network (CNN) model to make classification if detected infection with COVID-19 in X-ray, the results have gotten high training accuracy=99%

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Food Image Classification with Improved MobileNet Architecture and Data Augmentation

Cited by 44 publications

References 13 publications

Convolutional Neural Networks Using Enhanced Radiographs for Real-Time Detection of Sitophilus zeamais in Maize Grain

Convolutional Neural Networks Using Enhanced Radiographs for Real-Time Detection of Sitophilus zeamais in Maize Grain

Low-Cost CNN for Automatic Violence Recognition on Embedded System

Expansion dataset COVID-19 chest X-ray using data augmentation and histogram equalization

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