Object Recognition for Dental Instruments Using SSD-MobileNet

Ali, Hashir; Khursheed, Mahrukh; Fatima, Syeda Kulsoom; Shuja, Syed Muhammad; Noor, Shaheena

doi:10.1109/cisct.2019.8777441

Cited by 16 publications

(3 citation statements)

References 14 publications

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“…But hand recognition not only predicts the hand but also a probability of there is a hand based on an annotated image of a hand, the height of the bounding box, the width of the bounding box, horizontal coordinate of the center point of the bounding of box, and vertical coordinate of the center point of the bounding box. SSD has two component backbone models and the SSD head is a pre-trained model as a feature extractor [16]. Unlike SSD, Faster R-CNN runs the whole process at 7 frames per second (PFS).…”

Section: Intersectionoverunion(iou ) =mentioning

confidence: 99%

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Developing Amharic Sign Language Recognition Model for Amharic Characters Using Deep Learning Approach

Feyera

Seid

2021

Preprint

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Hearing-impaired people use Sign Language to communicate with each other as well as with other communities. Usually, they are unable to communicate with normal people. Most of the people without hearing disability do not understand the Sign Language and unable to understand hearing-impaired people. So, they need recognition of Sign Language to text. In this research, a model is optimized for the recognition of Amharic Sign Language to Amharic characters. A convolutional neural network model is trained on datasets gathered from a teacher of Amharic Sign Language. Frame extraction from Amharic Sign Language video, labeling and annotation, XML creation, generate TFrecord, and training models are major general steps followed for developing models to recognize Amharic Sign Language to characters. After training of the neural network is completed, the model is saved for recognition of Sign Language from a video system or from the frame of video. The accuracy of the model is the summation of confidence of individual alphabets correctly recognized divided by the number of alphabets presented for evaluation for Faster R-CNN and SSD. Hence, the mean average accuracy of the Faster R-CNN and Single-Shot Detector is found to be 98. 25% and 96 % respectively. The model is trained and evaluated for the character of the Amharic language. The research will continue to include the remaining words and sentence used in Amharic Sign Language to have a full- edged Sign Language recognition model to a complete system.

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Section: Intersectionoverunion(iou ) =mentioning

confidence: 99%

“…The accuracy of SSD is measured by mean average precision (mAP). SSD is detection is folded into two types: extract feature maps and apply convolutional filters to detect objects [16]. SSD uses visual geometry group16 (VGG-16) for extracting the feature map of Sign Language we used.…”

Section: Intersectionoverunion(iou ) =mentioning

confidence: 99%

Developing Amharic Sign Language Recognition Model for Amharic Characters Using Deep Learning Approach

Feyera

Seid

2021

Preprint

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“…59 and 94.3%,60 respectively. Ali et al reported on an algorithm for localizing and classifying dental instruments in image 61. Luo et al collected 3D motion signals in daily life using wearable devices and tested four algorithms classifying tooth brushing time and 15 tooth brushing motions.…”

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confidence: 99%

Application of Deep Learning in Dentistry and Implantology

Kang¹,

Duong²,

Park³

2020

kaomi

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Artificial intelligence and deep learning algorithms are infiltrating various fields of medicine and dentistry. The purpose of the current study was to review literatures applying deep learning algorithms to the dentistry and implantology. Electronic literature search through MEDLINE and IEEE Xplore library database was performed at 2019 October by combining free-text terms and entry terms associated with 'dentistry' and 'deep learning'. The searched literature was screened by title/abstract level and full text level. Following data were extracted from the included studies: information of author, publication year, the aim of the study, architecture of deep learning, input data, output data, and performance of the deep learning algorithm in the study. 340 studies were retrieved from the databases and 62 studies were included in the study. Deep learning algorithms were applied to tooth localization and numbering, detection of dental caries/periodontal disease/ periapical disease/oral cancerous lesion, localization of cephalometric landmarks, image quality enhancement, prediction and compensation of deformation error in additive manufacturing of prosthesis. Convolutional neural network was used for periapical radiograph, panoramic radiograph, or computed tomography in most of included studies. Deep learning algorithms are expected to help clinicians diagnose and make decisions by extracting dental data, detecting diseases and abnormal lesions, and improving image quality.

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Identification of Surgical Forceps Using YOLACT++

Memida,

Miura

2023

Mechanisms and Machine Science

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Object Recognition for Dental Instruments Using SSD-MobileNet

Cited by 16 publications

References 14 publications

Developing Amharic Sign Language Recognition Model for Amharic Characters Using Deep Learning Approach

Developing Amharic Sign Language Recognition Model for Amharic Characters Using Deep Learning Approach

Application of Deep Learning in Dentistry and Implantology

Identification of Surgical Forceps Using YOLACT++

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