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

arXiv: https://arxiv.org/abs/2003.07442 · HTML: https://mlsanity.com/paper/2003.07442/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2020.

## Built on by (most cited first, 1 of 1)

- [An advanced YOLOv3 method for small object detection](https://mlsanity.com/paper/2212.02809/) (2022)

## Abstract (arXiv)

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.