Relevant bibliographies by topics / TensorFlow Object Detection API 2

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  2. Dissertations / Theses
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Academic literature on the topic 'TensorFlow Object Detection API 2'

Author: Grafiati
Published: 28 June 2021
Last updated: 29 July 2025

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Journal articles on the topic "TensorFlow Object Detection API 2"

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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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Elshin, Кonstantin А., Еlena I. Molchanova, Мarina V. Usoltseva, and Yelena V. Likhoshway. "Automatic accounting of Baikal diatomic algae: approaches and prospects." Issues of modern algology (Вопросы современной альгологии), no. 2(20) (2019): 295–99. http://dx.doi.org/10.33624/2311-0147-2019-2(20)-295-299.

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Using the TensorFlow Object Detection API, an approach to identifying and registering Baikal diatom species Synedra acus subsp. radians has been tested. As a result, a set of images was formed and training was conducted. It is shown that аfter 15000 training iterations, the total value of the loss function was obtained equal to 0,04. At the same time, the classification accuracy is equal to 95%, and the accuracy of construction of the bounding box is also equal to 95%.
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Bojchuk, Aleksandr A., and Aleksandr G. Galkin. "Application of neural networks for diagnostics of contact network devices: modern approaches and prospects." Innotrans, no. 2 (2024): 10–12. http://dx.doi.org/10.20291/2311-164x-2024-2-10-12.

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The article discusses the prospects of using neural networks for information processing in the field of diagnostics of contact network devices. An overview of software and modern methods using neural networks has been presented, an analysis of software complexes based on fundamentally new methods of data processing obtained during the diagnosis of a contact network has been carried out. For further research, promising software solutions used for analyzing photo and video files have been noted: OpenCV, TensorFlow Object Detection API, YOLO, Amazon Rekognition Video и IBM Watson Visual Recogniti
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Petrov, Kirill A., Nadezhda N. Ivanova, and Sergei A. Solovev. "RECOGNIZING A TRANSFORMER OIL GAUGE IN AN IMAGE USING A PRE-TRAINED MOBILENETV2 FPN LITE MODEL." Vestnik Chuvashskogo universiteta, no. 4 (December 25, 2024): 107–16. https://doi.org/10.47026/1810-1909-2024-4-107-116.

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Oil gauges allow you to monitor the amount of oil in transformers, which is necessary to prevent its overheating and related negative consequences, such as deterioration of the insulating and cooling properties of the oil, increase in pressure inside the device and possible accidents. Most of the oil pointers do not have an electric output to indicate the measurement results, which complicates the process of reading the readings of the device. One of the options for solving this problem may be the use of intelligent methods of image analysis, which have recently been actively used in many scie
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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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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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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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K, Mohammed Nihad. "American Sign Language Translator." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 3430–33. http://dx.doi.org/10.22214/ijraset.2022.41924.

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Abstract: Sign language is one of the ancient and most natural form of language for communication. Sign Language is being used by many people in the world, but only a small part of the population knows how to interpret the language. Which makes it tough for the Deaf people to communicate with other people. We used the Tensorflow object detection API in our research, which is an open-source framework for training and deploying object detection models.
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Dissertations / Theses on the topic "TensorFlow Object Detection API 2"

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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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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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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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Agarwal, Kirti. "Object detection in refrigerators using Tensorflow." Thesis, 2018. https://dspace.library.uvic.ca//handle/1828/10464.

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Object Detection is widely used in many applications such as face detection, detecting vehicles and pedestrians on streets, and autonomous vehicles. Object detection not only includes recognizing and classifying objects in an image, but also localizes those objects and draws bounding boxes around them. Therefore, most of the successful object detection networks make use of neural network based image classifiers in conjunction with object detection techniques. Tensorflow Object Detection API, an open source framework based on Google's TensorFlow, allows us to create, train and deploy object de
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Neves, Lara Souto das. "Automatic defect detection in wind turbine blades: A Deep Learning Model Pipeline for Detection and Classification of Defects in Drone Images." Master's thesis, 2022. http://hdl.handle.net/10362/134713.

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Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science<br>The decarbonisation of the energy industry is key in the global approach to the climate emergency. Wind energy in particular, plays an important role in transitioning the global energy system to more sustainable sources. To do so, it must optimise O&M costs through a data-driven, predictive maintenance approach. When it comes to the maintenance of a wind turbine, the manual inspection of blade photographs - often taken by drones - is
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Books on the topic "TensorFlow Object Detection API 2"

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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 2"

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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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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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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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Choudhury, Madhumita, Durba Paul, Anal Acharya, Nisha Banerjee, and Debabrata Datta. "Face Mask and Social Distancing Detection in Real Time." In Advances in Web Technologies and Engineering. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-8306-0.ch011.

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With the recent outbreak and rapid transmission of the COVID-19 pandemic, the need for the people to follow social distancing and wear masks in public is only increasing. So, the main objective of this chapter is to build a machine learning model based on TensorFlow object detection API and YOLO Objection Detection that will determine a green and red rectangle around the face if the person detected in the camera wears or does not wear a mask, along with an email alert being sent to the authority in charge informing about a person's violation of face mask policy and will return a green or red b
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Kumar, Harish. "Intelligent Detection of Potholes Using SSD Algorithm and Auto-Alert Notification System for User." In Advances in Transdisciplinary Engineering. IOS Press, 2025. https://doi.org/10.3233/atde250006.

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Potholes are cracks on the road’s surface that leave a hole behind it. Reporting potholes to accountable bodies at an early stage can save many lives. Therefore, timely inspection and maintenance of potholes are required for smooth transportation. Traditional pothole detection methods are labor-intensive and time-consuming. This research focuses on such gaps and presents an intelligent detecting system that uses a smartphone camera, sensors, and gyroscope for real-time detection of potholes. The proposed model covers two essential functions: i). automated identification of potholes, and ii). n
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Xin, Chen, Minh Nguyen, and Wei Qi Yan. "Multiple Flames Recognition Using Deep Learning." In Handbook of Research on Multimedia Cyber Security. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2701-6.ch015.

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Identifying fire flames is based on object recognition which has valuable applications in intelligent surveillance. This chapter focuses on flame recognition using deep learning and its evaluations. For achieving this goal, authors design a Multi-Flame Detection scheme (MFD) which utilises Convolutional Neural Networks (CNNs). Authors take use of TensorFlow in deep learning with an NVIDIA GPU to train an image dataset and constructed a model for flame recognition. The contributions of this book chapter are: (1) data augmentation for flame recognition, (2) model construction for deep learning,
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More, Priyanka, Sachin Sakhare, Rahul Shelke, Saurabh Raut, Yugandhar Patil, and Darshan Vora. "Enhancing Independence Computer Vision-Based Object Detection Techniques for the Visually Impaired." In Modern Digital Approaches to Care Technologies for Individuals With Disabilities. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7560-0.ch014.

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This paper introduces an innovative solution leveraging the advanced YOLOv8 deep learning model to provide real-time object detection for over 2 billion blind and visually impaired individuals. Acting as a virtual “eye,” the system allows users to recognize objects and environments with high precision and speed. By integrating the Google Text-to-Speech API, it delivers intuitive voice guidance, offering immediate audio feedback for identified objects. This approach addresses the key challenges of accessibility and independence faced by the visually impaired and opens doors for more advanced as
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Conference papers on the topic "TensorFlow Object Detection API 2"

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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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