Evolving Perspectives: Innovations in Object Detection and Identification

Abstract

One of the most important developments in computer vision has been the creation of object detection and identification systems, which have allowed robots to perceive and understand visual data similarly to humans. These systems locate each object by drawing a bounding box around it, in addition to detecting and classifying every object in an image or video. This study suggests a novel method for item identification and detection that makes use of cutting-edge deep learning algorithms. The primary objective of this project is to develop a dependable and efficient system capable of accurately identifying and recognizing objects in images or video feeds. This system utilizes convolutional neural networks (CNNs), employing advanced architectures such as YOLO (You Only Look Once) or SSD (Single Shot MultiBox Detector), enabling real-time object detection. Finding an object's properties, such as color, texture, shape, or other features, is the first step in the detection process. Using the features mentioned, the system categorizes objects into multiple classes and assigns corresponding labels to each class. To improve object recognition and classification accuracy, the potential research area for innovations and use of more unique deep learning approaches is therefore examined. Model training, optimization, and dataset preparation are important project components. To enable thorough model training, an extensive collection of different datasets representing a broad range of item categories will be selected and annotated. We will use data augmentation and transfer learning techniques to improve the model's performance and generalization in various scenarios and object types. The creation of a software program or framework that incorporates the trained model will be the implementation phase's activity. Images or videos can be used as input for this program.