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Related Experiment Video

Updated: Jul 5, 2025

Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images

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Published on: August 31, 2022

Lightweight Tunnel Obstacle Detection Based on Improved YOLOv5.

Yingjie Li¹, Chuanyi Ma², Liping Li³

¹Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China.

Sensors (Basel, Switzerland)

|January 23, 2024

Summary

This study introduces an improved YOLOv5 model for autonomous obstacle detection in construction robots, enhancing safety. The new model significantly boosts detection speed and efficiency while maintaining high accuracy for hazardous environments.

Keywords:

improved YOLOv5 lightweight model object detection

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Area of Science:

Robotics and Automation
Computer Vision
Artificial Intelligence

Background:

Tunnel construction sites have a high incidence of accidents, necessitating the use of robots for hazardous tasks.
Current construction robots often lack autonomous obstacle avoidance, requiring manual control and posing safety risks.
Developing autonomous capabilities is crucial for enhancing worker safety and operational efficiency in construction.

Purpose of the Study:

To propose a lightweight and efficient object detection model for autonomous obstacle avoidance in construction robots.
To improve the speed and accuracy of obstacle detection in real-time applications.
To reduce the computational load and hardware requirements for robotic systems.

Main Methods:

A modified YOLOv5 architecture was developed, incorporating Shufflenet v2 as the backbone for reduced computational load.
A coordinate attention mechanism was integrated to improve feature representation learning.
GSConv was utilized in the neck module, and the upsampling method was optimized for enhanced efficiency and accuracy.

Main Results:

The proposed model achieved a 37% increase in detection speed with only a 1.5% reduction in accuracy.
Frame rate improved by approximately 54%, with a 74% decrease in parameter count and a 2.5 MB reduction in model size.
The optimized model demonstrated a balance between detection speed and accuracy, reducing hardware demands.

Conclusions:

The lightweight YOLOv5 model offers a viable solution for autonomous obstacle avoidance in construction robots.
The enhancements improve real-time performance and reduce system requirements, making autonomous robots more accessible.
This approach contributes to enhanced safety and efficiency in hazardous construction environments.