Object Detection Using Convolutional Neural Networks

Galvez, Reagan L.; Bandala, Argel A.; Dadios, Elmer P.; Vicerra, Ryan Rhay P.; Maningo, Jose Martin Z.

doi:10.1109/tencon.2018.8650517

Cited by 186 publications

(54 citation statements)

References 11 publications

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“…A particular type of multilayer perceptron is a CNN, but a simple neural network cannot learn complex features, unlike a deep learning architecture. CNNs have shown excellent performance in many applications [ 43 , 44 ], such as image classification, object detection, and medical image analysis. The main idea behind a CNN is that it can obtain local features from high layer inputs and transfer them to lower layers for more complex features.…”

Section: Methodsmentioning

confidence: 99%

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

Islam

Asraf

2020

Informatics in Medicine Unlocked

529

278

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Nowadays, automatic disease detection has become a crucial issue in medical science due to rapid population growth. An automatic disease detection framework assists doctors in the diagnosis of disease and provides exact, consistent, and fast results and reduces the death rate. Coronavirus (COVID-19) has become one of the most severe and acute diseases in recent times and has spread globally. Therefore, an automated detection system, as the fastest diagnostic option, should be implemented to impede COVID-19 from spreading. This paper aims to introduce a deep learning technique based on the combination of a convolutional neural network (CNN) and long short-term memory (LSTM) to diagnose COVID-19 automatically from X-ray images. In this system, CNN is used for deep feature extraction and LSTM is used for detection using the extracted feature. A collection of 4575 X-ray images, including 1525 images of COVID-19, were used as a dataset in this system. The experimental results show that our proposed system achieved an accuracy of 99.4%, AUC of 99.9%, specificity of 99.2%, sensitivity of 99.3%, and F1-score of 98.9%. The system achieved desired results on the currently available dataset, which can be further improved when more COVID-19 images become available. The proposed system can help doctors to diagnose and treat COVID-19 patients easily.

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Section: Methodsmentioning

confidence: 99%

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

Islam

Asraf

2020

Informatics in Medicine Unlocked

529

278

View full text Add to dashboard Cite

show abstract

“…Models such as the SSD-MobileNet model have an architecture that allows faster detection but with less accuracy. Models such as the Faster-RCNN model provide slower detection but greater accuracy (Galvez, Bandala, Dadios, Vicerra, & Maningo, 2018).…”

Section: The Methods Usedmentioning

confidence: 99%

İnsansız Su Altı Araçlarında Derin Öğrenme Yöntemleri

Ataner

Özdeş

Öztürk

et al. 2020

European Journal of Science and Technology

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Unmanned underwater vehicles (ROV/AUV) are robotic systems that can float underwater, are autonomous and remotely controlled. Nowadays, the Navy has focused on the operational use of unmanned underwater vehicles in the defense industry and in many areas, and has increased interest in this issue. Unmanned underwater vehicles. Unmanned underwater vehicles are carried out in civilian and military applications for different and varied purposes like protection of national sources, protection of environmental sources and researchs about that, miscellaneous construction activities, police of coastal and country. Also they can use civil and military applications and they helped they have helped with much of the academic and industrial research done in recent years. To sum up they are remotely controlled vehicles with observation and exploration features. This article discusses image processing and deep learning techniques in unmanned underwater vehicles. Also it presents an in-depth review of the artificial intelligence technique and aims to contribute to our country's defense industry. The options that will enable the vehicle to succeed in autonomous missions are mentioned. The Raspberry Pi 3 microprocessor was used in autonomous missions. The Raspberry Pi Camera Module, which is compatible with the Raspberry Pi 3, is preferred. Python was used as a programming language during software process. Objects in the images taken from the camera have been identified using the OpenCV library and deep learning. The TensorFlow library which deep learning library, was used for object detection and tracking. At the beginning The Faster-RCNN-Inception-V2 model was used as the Model. However, Faster-RCNN-Inception-V2 model and Raspberry Pi 3 FPS cooperation working did not show a good performance. For this reason, the SSDLite-MobileNet-V2 model, which is fast enough for most real-time object detection applications, is preferred.

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“…About 300 research contributions presented that cover many aspects of the generic object detection framework like object proposal generation, object feature representation, training strategies, context modeling, popular datasets, and evaluation metrics. CNN [5] used to build mobile robots, which perform certain tasks like surveillance, navigation, and explosive ordnance disposal (EOD). Using vision systems within the robots makes aware that what sort of environment it had been and what sort of objects are there in the environment.…”

Section: Related Workmentioning

confidence: 99%

“…Some Machine Learning approaches are Viola-Jones object detection framework based on Haar features, Scaleinvariant feature transform (SIFT) [2], and Histogram of oriented gradients (HOG) features. On the contrary, deep learning techniques [3,4] are adept at doing end-to-end object detection without notably defining features, and are typically hinge on Convolutional Neural Networks (CNN) [5,6], Region based-CNN [7], You Only Look Once (YOLO [8], YOLOV2 [9], YOLOV3 [10]), etc.…”

Section: Introductionmentioning

confidence: 99%

Object Detection Using Stacked YOLOv3

Padmanabula¹,

Puvvada²,

Venkatramaphanikumar³

et al. 2020

ISI

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Object detection is a stimulating task in the applications of computer vision. It is gaining a lot of attention in many real-time applications such as detection of number plates of suspect cars, identifying trespassers under surveillance areas, detecting unmasked faces in security gates during the COVID-19 period, etc. Region-based Convolution Neural Networks(R-CNN), You only Look once (YOLO) based CNNs, etc., comes under Deep Learning approaches. In this proposed work, an improved stacked Yolov3 model is designed for the detection of objects by bounding boxes. Hyperparameters are tuned to get optimum performance. The proposed model evaluated using the COCO dataset, and the performance is better than other existing object detection models. Anchor boxes are used for overlapping objects. After removing all the predicted bounding boxes that have a low detection probability, bounding boxes with the highest detection probability are selected and eliminated all the bounding boxes whose Intersection Over Union value is higher than 0.4. Non-Maximal Suppression (NMS) is used to only keep the best bounding box. In this experimentation, we have tried with various range of values, but finally got better result at threshold 0.5.

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Object Detection Using Convolutional Neural Networks

Cited by 186 publications

References 11 publications

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

İnsansız Su Altı Araçlarında Derin Öğrenme Yöntemleri

Object Detection Using Stacked YOLOv3

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