Web-Page Interfaced Real-Time Object Detection Using TensorFlow

Singh, Govindra; Yadav, Anurag; Bhardwaj, Indu; Chauhan, Usha

doi:10.1109/icac3n53548.2021.9725742

Cited by 3 publications

(1 citation statement)

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“…With the use of these technologies, CV-based real-time object detection can detect, locate, and trace an object from an image or a video. The authors propose a method for integrating this real-time system into a web page in Singh et al (2021) . The TensorFlow object detection API is used to detect objects, and live video is streamed through a password-protected login web page that can be accessed from any device.…”

Section: Related Workmentioning

confidence: 99%

Design and implementation of real-time object detection system based on single-shoot detector and OpenCV

Wahab

Ullah²,

Shah³

et al. 2022

Front. Psychol.

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Computer vision (CV) and human–computer interaction (HCI) are essential in many technological fields. Researchers in CV are particularly interested in real-time object detection techniques, which have a wide range of applications, including inspection systems. In this study, we design and implement real-time object detection and recognition systems using the single-shoot detector (SSD) algorithm and deep learning techniques with pre-trained models. The system can detect static and moving objects in real-time and recognize the object’s class. The primary goals of this research were to investigate and develop a real-time object detection system that employs deep learning and neural systems for real-time object detection and recognition. In addition, we evaluated the free available, pre-trained models with the SSD algorithm on various types of datasets to determine which models have high accuracy and speed when detecting an object. Moreover, the system is required to be operational on reasonable equipment. We tried and evaluated several deep learning structures and techniques during the coding procedure and developed and proposed a highly accurate and efficient object detection system. This system utilizes freely available datasets such as MS Common Objects in Context (COCO), PASCAL VOC, and Kitti. We evaluated our system’s accuracy using various metrics such as precision and recall. The proposed system achieved a high accuracy of 97% while detecting and recognizing real-time objects.

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Section: Related Workmentioning

confidence: 99%