Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2025.073662
Journal Menu
Special Issues
Table of Content

All issues

Online First

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

ARTICLE

ADCP-YOLO: A High-Precision and Lightweight Model for Violation Behavior Detection in Smart Factory Workshops

Changjun Zhou1, Dongfang Chen1, Chenyang Shi1, Taiyong Li2,*
1 School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China
2 School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, 611130, China
* Corresponding Author: Taiyong Li. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.073662

Received 23 September 2025; Accepted 11 November 2025; Published online 12 December 2025

Abstract

With the rapid development of smart manufacturing, intelligent safety monitoring in industrial workshops has become increasingly important. To address the challenges of complex backgrounds, target scale variation, and excessive model parameters in worker violation detection, this study proposes ADCP-YOLO, an enhanced lightweight model based on YOLOv8. Here, “ADCP” represents four key improvements: Alterable Kernel Convolution (AKConv), Dilated-Wise Residual (DWR) module, Channel Reconstruction Global Attention Mechanism (CRGAM), and Powerful-IoU loss. These components collaboratively enhance feature extraction, multi-scale perception, and localization accuracy while effectively reducing model complexity and computational cost. Experimental results show that ADCP-YOLO achieves a mAP of 90.6%, surpassing YOLOv8 by 3.0% with a 6.6% reduction in parameters. These findings demonstrate that ADCP-YOLO successfully balances accuracy and efficiency, offering a practical solution for intelligent safety monitoring in smart factory workshops.

Keywords

YOLO; violation behavior detection; AKConv; CRGAM; DWR; Powerful-IoU

  • 277

    View

  • 45

    Download

  • 0

    Like

Related articles
Copyright© 2026 Tech Science Press
© 1997-2026 TSP (Henderson, USA) unless otherwise stated

Share Link