OBJECT DETECTION IN AN IMAGE

Saima Ansari; Ayushi Gondane; Ayman Firdous; Sanober Tahseen; Mohd Arhan Kaif

doi:10.15680/IJIRCCE.2023.1104146

Saima Ansari Assistant Professor , Computer Science and Engineering, Anjuman College of Engineering and Technology, Nagpur, India
Ayushi Gondane Student, Computer Science and Engineering, Anjuman College of Engineering and Technology, Nagpur, India
Ayman Firdous Student, Computer Science and Engineering, Anjuman College of Engineering and Technology, Nagpur, India
Sanober Tahseen Student, Computer Science and Engineering, Anjuman College of Engineering and Technology, Nagpur, India
Mohd Arhan Kaif Student, Computer Science and Engineering, Anjuman College of Engineering and Technology, Nagpur, India

DOI: https://doi.org/10.15680/IJIRCCE.2023.1104146

Keywords: object detection, deep learning, convolutional neural networks, CNNs, region proposal, object classification, COCO, TensorFlow.js, MobileNet, ResNet, Single Shot MultiBox Detector.

Abstract

This project aims to develop an object detection system using TensorFlow.js, a JavaScript library for building and training machine learning models in the browser or Node.js environment. Object detection is a critical task in computer vision, with various applications such as image and video analysis, robotics, and autonomous driving. The proposed system uses a pre-trained deep learning model such as MobileNet or ResNet as the feature extractor, and applies the Single Shot MultiBox Detector (SSD) algorithm for object detection. The system is trained and fine-tuned on a custom dataset, and the resulting model is integrated with a web-based user interface for real-time object detection. The performance of the system is evaluated on a test set and compared with existing object detection models