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Related Experiment Video

Updated: May 5, 2026

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

YOLOv5 with Channel Attention and Feature Fusion for Wheel Surface Defect Detection.

Juanjuan Gao^1,2, Yuanchao Wang², Xiuye Xia²

¹Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao 066004, China.

Sensors (Basel, Switzerland)

|May 4, 2026

Summary

This summary is machine-generated.

This study introduces an improved YOLOv5 algorithm for detecting wheel surface defects, enhancing vehicle safety. The new method effectively identifies small defects on curved surfaces, ensuring real-time detection accuracy.

Keywords:

YOLO deep learning machine vision object detection wheel surface defect detection

Related Experiment Videos

Last Updated: May 5, 2026

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

Area of Science:

Automotive Engineering
Computer Vision
Artificial Intelligence

Background:

Wheel surface defects can compromise vehicle safety.
Detecting diverse wheel defects on curved surfaces is challenging.
Current methods may lack accuracy or real-time capabilities.

Purpose of the Study:

To develop an advanced algorithm for accurate and efficient wheel surface defect detection.
To address the challenges posed by the variety and complexity of wheel surface defects.
To enable real-time monitoring and identification of wheel surface anomalies.

Main Methods:

An improved YOLOv5 algorithm was proposed for wheel surface defect detection.
The algorithm incorporates the efficient channel attention mechanism (ECA) and adaptive spatial feature fusion (ASFF) detection head.
A GSConv+SlimNeck structure was utilized for enhanced feature extraction and fusion.

Main Results:

The improved YOLOv5 algorithm demonstrated higher mean Average Precision (mAP) and detection accuracy.
The method effectively enhances the detection of small objects on curved surfaces.
High frames per second (FPS) were maintained, meeting real-time detection requirements.

Conclusions:

The enhanced YOLOv5 algorithm provides a robust solution for real-time wheel surface defect detection.
The integration of ECA and ASFF significantly improves detection performance.
This technology contributes to improved automotive safety through reliable defect identification.