Relevant bibliographies by topics / Tensorflow Object Detection API

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Tensorflow Object Detection API'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Tensorflow Object Detection API"

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Aysha, Ms. "Vehicle Detection and Traffic Prediction." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 1791–94. http://dx.doi.org/10.22214/ijraset.2021.38269.

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Abstract: On the road, vehicle detection processes are utilized for vehicle tracking, vehicle counting, vehicle speed, and traffic analysis. For vehicle detection, the Tensorflow object detection API method is employed. The Object Detection API in Tensorflow is a powerful tool that allows anyone to easily design and deploy effective picture recognition applications. Another way to control traffic is to use a traffic control system. Multiple linear regression is utilized to regulate the traffic system, while the OpenCV approach is used to identify vehicle speed. A system for fine payment is als
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Salunkhe, Akilesh, Manthan Raut, Shayantan Santra, and Sumedha Bhagwat. "Android-based object recognition application for visually impaired." ITM Web of Conferences 40 (2021): 03001. http://dx.doi.org/10.1051/itmconf/20214003001.

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Detecting objects in real-time and converting them into an audio output was a challenging task. Recent advancement in computer vision has allowed the development of various real-time object detection applications. This paper describes a simple android app that would help the visually impaired people in understanding their surroundings. The information about the surrounding environment was captured through a phone’s camera where real-time object recognition through tensorflow’s object detection API was done. The detected objects were then converted into an audio output by using android’s text-t
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Hayati, Lilis Nur, Anik Nur Handayani, Wahyu Sakti Gunawan Irianto, Rosa Andrie Asmara, Dolly Indra, and Muhammad Fahmi. "Classifying BISINDO Alphabet using TensorFlow Object Detection API." ILKOM Jurnal Ilmiah 15, no. 2 (2023): 358–64. http://dx.doi.org/10.33096/ilkom.v15i2.1692.358-364.

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Indonesian Sign Language (BISINDO) is one of the sign languages used in Indonesia. The process of classifying BISINDO can be done by utilizing advances in computer technology such as deep learning. The use of the BISINDO letter classification system with the application of the MobileNet V2 FPNLite SSD model using the TensorFlow object detection API. The purpose of this study is to classify BISINDO letters A-Z and measure the accuracy, precision, recall, and cross-validation performance of the model. The dataset used was 4054 images with a size of consisting of 26 letter classes, which were tak
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Sharma, Rishabh. "Blindfold: A Smartphone based Object Detection Application." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 1268–73. http://dx.doi.org/10.22214/ijraset.2021.35091.

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With the advancement of computing power of Smartphones, they seem to be a better option to be used as an Assistive Technology for the visually impaired. In this paper we have discussed an application which allows visually impaired users to detect objects of their choice in their environment. We have made use of the Tensorflow Lite Application Programmable Interface (API), an API by Tensorflow which specifically runs models on an Android Smartphone. We have discussed the architecture of the API and the application itself. We have discussed the performance of various types of models such as Mobi
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Arun Kharve, Aniruddha. "Leveraging TensorFlow Lite and Camera2 API for Efficient Real-Time Object Detection in Android Apps Using Kotlin." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04033.

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Abstract - This study uses Kotlin, Camera2 API, and TensorFlow Lite to design and construct an Android application for real-time object identification. The project aims to provide an efficient mobile solution that identifies and classifies objects through the phones camera in real time. To enhance accuracy and performance across various devices, the app integrates four pre-trained lightweight machine learning models: MobileNetV1, EfficientNet-Lite, EfficientNet-Lite1, and EfficientNet-Lite2. Additional features include image selection from the gallery, threshold-based object detection, and cla
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Birambole, Aniket, Pooja Bhagat, Bhavesh Mhatre, and Prof Aarti Abhyankar. "Blind Person Assistant: Object Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (2022): 1168–72. http://dx.doi.org/10.22214/ijraset.2022.40850.

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Abstract: It’s a known fact that estimated number of blind persons in the world is about 285 million, approximately equal to the 20% of the Indian Population. They are mostly dependent on someone for even accessing their basic daily needs. In our project, we used TensorFlow, it's a new library from Google. TensorFlow model our neural networks. The TensorFlow Object Detection API is used to detect many objects. We have Introduce an algorithm (SSD). SSD uses a similar phase while training, to match the appropriate anchor box with the bounding boxes of each ground truth object within an image. Es
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Selvaganapathy, Shymala Gowri, Hema Priya, N, Rathika P.D., and Venkatachalam K. "OBJECT DETECTION USING SEMI SUPERVISED LEARNING METHODS." ICTACT Journal on Soft Computing 12, no. 4 (2022): 2723–28. http://dx.doi.org/10.21917/ijsc.2022.0388.

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Object detection is used to identify objects in real time using some deep learning algorithms. In this work, wheat plant data set around the world is collected to study the wheat heads. Using global data, a common solution for measuring the amount and size of wheat heads is formulated. YOLO V3 (You Look Only Once Version 3) and Faster RCNN is a real time object detection algorithm which is used to identify objects in videos and images. The global wheat detection dataset is used for the prediction which contains 3000+ training images and few test images with csv files which have information abo
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Ganorkar, Prof Sandeep. "Java Based Object Recognition Application for Visually Impaired." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem26579.

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It was difficult to detect items in real time and turn them into an auditory output. Many real-time object identification apps have been made possible by recent developments in computer vision. This essay explains a basic Android application. that would make it easier for those who are blind to comprehend their surroundings. Data about the surrounding area was recorded using the phone's camera. where the object detection API of Tensor flow was used to perform real-time object recognition. Android's text-to-speech feature was then used to turn the objects that were recognized into an audio outp
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Arwa, Mohammed Taqi, Al-Azzo Fadwa, Awad Ahmed, and Milanova Mariofanna. "Skin Lesion Detection by Android Camera based on SSD- Mobilenet and TensorFlow Object Detection API." American Journal of Advanced Research 3, no. 1 (2019): 6–12. https://doi.org/10.5281/zenodo.3264023.

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With the fast evolution of medical imaging study, a great interest in skin cancer detection has been investigated with numerous computer algorithms. Generally, skin lesions are examined with a limited quantity of ground truth labeling. The most important part of the medical image’s detection is calculating the localization function which is normally evaluated on the Intersection over Union threshold (IoU). It helps to locate the lesion accurately to collect dominant features of the skin lesion. In this work, an object localization for skin lesion detection has been proposed using SSD- Mo
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K. Pujitha, J. Indu, B. Sasi Vardhan, P. Sandeep Kumar, and Mrs. G. Ramadevi. "Traffic Signal Violation Detection System." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 2766–71. https://doi.org/10.32628/cseit2511141.

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Traffic signal violations are a major cause of accidents and traffic congestion. This project presents an automated Traffic Signal Violation Detection System using Deep Learning-based Object Detection. The system leverages SSD MobileNet V1, a pre-trained Convolutional Neural Network (CNN), to detect and classify traffic signals in real-time. Using the TensorFlow Object Detection API, the model identifies traffic lights and determines violations based on detected signals. The approach integrates image processing, real-time object detection, and violation recognition, providing an intelligent tr
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Dissertations / Theses on the topic "Tensorflow Object Detection API"

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Furundzic, Bojan, and Fabian Mathisson. "Dataset Evaluation Method for Vehicle Detection Using TensorFlow Object Detection API." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43345.

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Recent developments in the field of object detection have highlighted a significant variation in quality between visual datasets. As a result, there is a need for a standardized approach of validating visual dataset features and their performance contribution. With a focus on vehicle detection, this thesis aims to develop an evaluation method utilized for comparing visual datasets. This method was utilized to determine the dataset that contributed to the detection model with the greatest ability to detect vehicles. The visual datasets compared in this research were BDD100K, KITTI and Udacity,
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Černil, Martin. "Automatická detekce ovládacích prvků výtahu zpracováním digitálního obrazu." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-444987.

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This thesis deals with the automatic detection of elevator controls in personal elevators through digital imaging using computer vision. The theoretical part of the thesis goes through methods of image processing with regards to object detection in image and research of previous solutions. This leads to investigation into the field of convolutional neural networks. The practical part covers the creation of elevator controls image dataset, selection, training and evaluation of the used models and the implementation of a robust algorithm utilizing the detection of elevator controls. The concluss
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Horák, Martin. "Sémantický popis obrazovky embedded zařízení." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-413261.

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Tato diplomová práce se zabývá detekcí prvků uživatelského rozhraní na obrázku displejetiskárny za použití konvolučních neuronových sítí. V teoretické části je provedena rešeršesoučasně používaných architektur pro detekci objektů. V praktické čísti je probrána tvorbagalerie, učení a vyhodnocování vybraných modelů za použití Tensorflow ObjectDetectionAPI. Závěr práce pojednává o vhodnosti vycvičených modelů pro zadaný úkol.
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Alsing, Oscar. "Mobile Object Detection using TensorFlow Lite and Transfer Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233775.

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With the advancement in deep learning in the past few years, we are able to create complex machine learning models for detecting objects in images, regardless of the characteristics of the objects to be detected. This development has enabled engineers to replace existing heuristics-based systems in favour of machine learning models with superior performance. In this report, we evaluate the viability of using deep learning models for object detection in real-time video feeds on mobile devices in terms of object detection performance and inference delay as either an end-to-end system or feature
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Mustamo, P. (Pirkko). "Object detection in sports:TensorFlow Object Detection API case study." Bachelor's thesis, University of Oulu, 2018. http://urn.fi/URN:NBN:fi:oulu-201802081173.

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Object detection is widely used in the world of sports, its users including training staff, broadcasters and sports fans. Neural network based classifiers are used together with other object detection techniques. The aim of this study was to explore the modern open source based solutions for object detection in sports, in this case for detecting football players. TensorFlow Object Detection API, an open source framework for object detection related tasks, was used for training and testing an SSD (Single-Shot Multibox Detector) with Mobilenet- model. The model was tested as a) pre-trained and b
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Manousian, Jonathan. "Digitalisering av handskrivna siffror på fysiska formulär : Utvärdering av tillförlitlighet och träningstid." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-39343.

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Inom arbetslivet finns situationer i vilka vi kan utnyttja digitalisering för att förenkla och effektivisera arbetet. Ett exempel är den analoga hanteringen av fysiska formulär. Oftast överförs data från fysiska formulär till datorn manuellt. Syftet med detta projekt är att effektivisera den generella hanteringen av pappersformulär genom inskanning. Detta kan göras genom att utnyttja en beskärningsfunktion vid inskanningen. Beskärningen används för att beskära bort irrelevant data från formuläret och därmed framhävs det som ska skannas in. Därefter kan objektigenkänning användas för att känna
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Nyberg, Selma. "Video Recommendation Based on Object Detection." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-351122.

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In this thesis, various machine learning domains have been combined in order to build a video recommender system that is based on object detection. The work combines two extensively studied research fields, recommender systems and computer vision, that also are rapidly growing and popular techniques on commercial markets. To investigate the performance of the approach, three different content-based recommender systems have been implemented at Spotify, which are based on the following video features: object detections, titles and descriptions, and user preferences. These systems have then been 
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Ferm, Oliwer. "Real-time Object Detection on Raspberry Pi 4 : Fine-tuning a SSD model using Tensorflow and Web Scraping." Thesis, Mittuniversitetet, Institutionen för elektronikkonstruktion, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-39455.

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Edge AI is a growing area. The use of deep learning on low cost machines, such as the Raspberry Pi, may be used more than ever due to the easy use, availability, and high performance. A quantized pretrained SSD object detection model was deployed to a Raspberry Pi 4 B to evaluate if the throughput is sufficient for doing real-time object recognition. With input size of 300x300, an inference time of 185 ms was obtained. This is an improvement as of the previous model; Raspberry Pi 3 B+, 238 ms with a input size of 96x96 which was obtained in a related study. Using a lightweight model is for the
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Michelini, Mattia. "Barcode detection by neural networks on Android mobile platforms." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21080/.

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Lo scopo di questa esperienza di tesi è stato quello di fare un confronto sul tema delle reti neurali e, in particolare, sul modo di portare la potenza inferenziale di questi modelli nel mondo mobile. Il caso di studio interessa i codici a barre e, nello specifico, l’obiettivo è stato quello di riuscire a identificare la zona in cui questi si trovavano, in modo poi da avere una zona minore da indagare con un altro algoritmo specifico per la decodifica (cosa che esula dallo scopo della tesi). Questo è chiaramente un problema di object detection e, per risolverlo, ho esplorato due differenti tip
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Rexhaj, Kastriot. "Machine visual feedback through CNN detectors : Mobile object detection for industrial application." Thesis, Mittuniversitetet, Institutionen för elektronikkonstruktion, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-36467.

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This paper concerns itself with object detection as a possible solution to Valmet’s quest for a visual-feedback system that can help operators and other personnel to more easily interact with their machines and equipment. New advancements in deep learning, specifically CNN models, have been exploring neural networks with detection-capabilities. Object detection has historically been mostly inaccessible to the industry due the complex solutions involving various tricky image processing algorithms. In that regard, deep learning offers a more easily accessible way to create scalable object detect
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Books on the topic "Tensorflow Object Detection API"

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Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, Deep RL, Unsupervised Learning, Object Detection and Segmentation, and More. de Gruyter GmbH, Walter, 2020.

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Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, Deep RL, Unsupervised Learning, Object Detection and Segmentation, and More, 2nd Edition. Packt Publishing, Limited, 2020.

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Book chapters on the topic "Tensorflow Object Detection API"

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Srivastava, Sharvani, Amisha Gangwar, Richa Mishra, and Sudhakar Singh. "Sign Language Recognition System Using TensorFlow Object Detection API." In Communications in Computer and Information Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96040-7_48.

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Srichitra, S., and S. Sreeja. "Implementation of ROS-Based Mobile Robots with Few Shot Object Detection Using TensorFlow API." In Soft Computing: Theories and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0707-4_42.

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Sharma, Anushka, Trapti Mishra, Jyoti Kukade, Aadya Golwalkar, and Hrithik Tomar. "Object Detection Using TensorFlow." In ICT Analysis and Applications. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6568-7_31.

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Paper, David. "Object Detection." In State-of-the-Art Deep Learning Models in TensorFlow. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7341-8_13.

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Rane, Milind, Aseem Patil, and Bhushan Barse. "Real Object Detection Using TensorFlow." In Lecture Notes in Electrical Engineering. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8715-9_5.

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Rizvi, Syed Wajahat Abbas, Rashmi Priya, and Manoj Kumar Misra. "Object Detection Modeling Using TensorFlow." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3256-5_26.

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Kaur, Harleen, Arisha Mirza, Bhavya Alankar, and Ritu Chauhan. "Sign Language Detection Using Tensorflow Object Detection." In Meta Heuristic Techniques in Software Engineering and Its Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11713-8_20.

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Howal, Sadanand, Aishwarya Jadhav, Chandrakirti Arthshi, Sapana Nalavade, and Sonam Shinde. "Object Detection for Autonomous Vehicle Using TensorFlow." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30465-2_11.

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Goel, Vartika, Deepak Arora, and Sheenu Rizvi. "Object Detection Using TensorFlow 2 and Amazon SageMaker." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9811-1_29.

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Menaka, R., and G. Padmavathi. "Selection of Robust Text-Based CAPTCHA Using TensorFlow Object Detection Method." In Proceedings of Data Analytics and Management. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6550-2_25.

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Conference papers on the topic "Tensorflow Object Detection API"

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A S, Renugadevi, D. Kalaiabirami, Guhan R, et al. "Sign Language Detecting System Using Tensorflow Object Detection API." In 2024 Third International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN). IEEE, 2024. http://dx.doi.org/10.1109/icstsn61422.2024.10670880.

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Kausik, M. D. Ashfakul Karim, Ahamed A. H. Sunny, Rahat K. Bhuiyan, M. H. Bappy, Adib Bin Rashid, and C. M. A. Rahman. "Real-Time Detection of Defective Products of a Tortilla Machine Production Line Using TensorFlow Object Detection API and OpenCV." In 2024 IEEE 3rd International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things (RAAICON). IEEE, 2024. https://doi.org/10.1109/raaicon64172.2024.10928521.

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Barba-Guaman, Luis, Jose Eugenio Naranjo, and Anthony Ortiz. "Object detection in rural roads using Tensorflow API." In 2020 International Conference of Digital Transformation and Innovation Technology (Incodtrin). IEEE, 2020. http://dx.doi.org/10.1109/incodtrin51881.2020.00028.

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Kannan, Raadhesh, Chin Ji Jian, and XiaoNing Guo. "Adversarial Evasion Noise Attacks Against TensorFlow Object Detection API." In 2020 15th International Conference for Internet Technology and Secured Transactions (ICITST). IEEE, 2020. http://dx.doi.org/10.23919/icitst51030.2020.9351331.

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Hsieh, Cheng-Hsiung, Dung-Ching Lin, Cheng-Jia Wang, Zong-Ting Chen, and Jiun-Jian Liaw. "Real-Time Car Detection and Driving Safety Alarm System With Google Tensorflow Object Detection API." In 2019 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2019. http://dx.doi.org/10.1109/icmlc48188.2019.8949265.

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Kilic, Irfan, and Galip Aydin. "Traffic Sign Detection And Recognition Using TensorFlow’ s Object Detection API With A New Benchmark Dataset." In 2020 International Conference on Electrical Engineering (ICEE). IEEE, 2020. http://dx.doi.org/10.1109/icee49691.2020.9249914.

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Rosol, Marcin. "Application of the TensorFlow object detection API to high speed videos of pyrotechnics for velocity calculations." In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, edited by Tien Pham, Latasha Solomon, and Katie Rainey. SPIE, 2020. http://dx.doi.org/10.1117/12.2557526.

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Grimes, Dupri, and Damian Valles. "Performance Analysis of TensorFlow2 Object Detection API Models for Engineering Site Surveillance Applications." In 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2023. http://dx.doi.org/10.1109/ccwc57344.2023.10099270.

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Lim, Kah Yee, Joan Hau, and Yiqi Tew. "Computer Performance Evaluation for Virtual Classroom with Artificial Intelligence Features." In International Conference on Digital Transformation and Applications (ICDXA 2021). Tunku Abdul Rahman University College, 2021. http://dx.doi.org/10.56453/icdxa.2021.1008.

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The advancement of computer technology allows students to interact with Artificial Intelligence (AI) through smart classrooms. Smart classroom is one of the latest technology enhanced learning (TEL) which allows the classroom and students to interact during the learning process. Currently, smart classrooms are believed to change current dull teaching methods and enhance the students’ learning experience. Therefore, the proposed paper is a comprehensive study of applying artificial intelligence features to an intelligent classroom system (a.k.a virtual classroom system) that provides face detec
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Pachipala, Yellamma, M. Harika, B. Aakanksha, and M. Kavitha. "Object Detection using TensorFlow." In 2022 International Conference on Electronics and Renewable Systems (ICEARS). IEEE, 2022. http://dx.doi.org/10.1109/icears53579.2022.9752263.

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