# Real Time Multi-Class Object Detection and Recognition Using Vision Augmentation Algorithm

Source: [Real Time Multi-Class Object Detection and Recognition Using Vision Augmentation Algorithm](<https://www.sota2.com/research/paper/68fa68b71e209131cea1db17>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:43:58.904Z

Authors: Al-Akhir Nayan, Joyeta Saha, Ahamad Nokib Mozumder, Khan Raqib Mahmud, Abul Kalam Al Azad

Published: 2020-03-17T01:08:24.000Z

## Abstract

The aim of this research is to detect small objects with low resolution and noise. The existing real time object detection algorithm is based on the deep neural network of convolution need to perform multilevel convolution and pooling operations on the entire image to extract a deep semantic characteristic of the image. The detection models perform better for large objects. The features of existing models do not fully represent the essential features of small objects after repeated convolution operations. We have introduced a novel real time detection algorithm which employs upsampling and skip connection to extract multiscale features at different convolution levels in a learning task resulting a remarkable performance in detecting small objects. The detection precision of the model is shown to be higher and faster than that of the state-of-the-art models.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2003.07442>)

[PDF](<https://arxiv.org/pdf/2003.07442.pdf>)