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 Li^2,*

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:

Computers, Materials & Continua 2026, 86(3), 82 https://doi.org/10.32604/cmc.2025.073662

Received 23 September 2025; Accepted 11 November 2025; Issue published 12 January 2026

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.

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Keywords

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