DeepSort-3C: Background-Removed Object Dataset for Intelligent Conveyor Belt Classification

Description

An AI-powered object detection and sorting dataset designed for classifying everyday household objects into three categories: Black, Transparent, and Colourful. The dataset includes images of common home items such as toys, containers, bottles, tools, and other domestic objects collected in real-world environments for deep learning–based classification and intelligent conveyor belt routing applications. All images were preprocessed by removing the background of each object to improve feature extraction and increase classification performance. This background elimination helps deep learning models focus more effectively on object properties such as color, texture, and shape while reducing unnecessary environmental noise. The dataset is organized into the following three classes: Colourful objects: 68 images Transparent objects: 24 images Black objects: 46 images This dataset can support research and development in: Object detection Image classification Smart conveyor belt systems AI-based industrial automation Computer vision applications The dataset is particularly useful for training deep learning models intended for automated object sorting and routing systems in industrial and smart manufacturing environments.

Institutions

• Bahria University

  Islamabad, Islamabad

Categories

Licence