# Real-time object detection method based on improved YOLOv4-tiny

arXiv: https://arxiv.org/abs/2011.04244 · HTML: https://mlsanity.com/paper/2011.04244/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2020.

## Built on by (most cited first, 9 of 9)

- [Accurate online training of dynamical spiking neural networks through Forward Propagation Through Time](https://mlsanity.com/paper/2112.11231/) (2021)
- [APARATE: Adaptive Adversarial Patch for CNN-based Monocular Depth Estimation for Autonomous Navigation](https://mlsanity.com/paper/2303.01351/) (2023)
- [AlertTrap: A study on object detection in remote insects trap monitoring system using on-the-edge deep learning platform](https://mlsanity.com/paper/2112.13341/) (2021)
- [Automated visual inspection of silicon detectors in CMS experiment](https://mlsanity.com/paper/2206.02572/) (2022)
- [Real-Time Automated donning and doffing detection of PPE based on Yolov4-tiny](https://mlsanity.com/paper/2407.17471/) (2024)
- [SETA: Statistical Fault Attribution for Compound AI Systems](https://mlsanity.com/paper/2601.19337/) (2026)
- [Towards a Fully Autonomous UAV Controller for Moving Platform Detection and Landing](https://mlsanity.com/paper/2210.08120/) (2022)
- [An Energy-Efficient Smart Bus Transport Management System with Blind-Spot Collision Detection Ability](https://mlsanity.com/paper/2601.01274/) (2026)
- [A Study on Real-time Object Detection using Deep Learning](https://mlsanity.com/paper/2602.15926/) (2026)

## Abstract (arXiv)

The "You only look once v4"(YOLOv4) is one type of object detection methods in deep learning. YOLOv4-tiny is proposed based on YOLOv4 to simple the network structure and reduce parameters, which makes it be suitable for developing on the mobile and embedded devices. To improve the real-time of object detection, a fast object detection method is proposed based on YOLOv4-tiny. It firstly uses two ResBlock-D modules in ResNet-D network instead of two CSPBlock modules in Yolov4-tiny, which reduces the computation complexity. Secondly, it designs an auxiliary residual network block to extract more feature information of object to reduce detection error. In the design of auxiliary network, two consecutive 3x3 convolutions are used to obtain 5x5 receptive fields to extract global features, and channel attention and spatial attention are also used to extract more effective information. In the end, it merges the auxiliary network and backbone network to construct the whole network structure of improved YOLOv4-tiny. Simulation results show that the proposed method has faster object detection than YOLOv4-tiny and YOLOv3-tiny, and almost the same mean value of average precision as the YOLOv4-tiny. It is more suitable for real-time object detection.