Abstract: In live-line work scenarios, it is essential for workers to wear electric field shielding clothing to prevent fatal accidents caused by electric shock. Accordingly, this study developed an electric field shielding clothing detection system for live-line working environments based on the YOLOv11 framework. Previous research has explored intelligent wearable detection systems for personal protective equipment such as safety helmets. However, compared to safety helmets, electric field shielding clothing comes in more varieties and is more challenging to identify. To address the challenges mentioned above, this study constructed a dual-layer detection model for operator detection and electric field shielding clothing detection in live-line work scenarios. The first layer employs an improved detection transformer (IDETR) to locate operators within the environment. The second layer, based on the YOLOv11 framework integrated with recursive gated convolution (GnConv), is designed to classify three types of personal protective equipment, including electric field shield clothing, electric field shield masks, and electric field shield gloves. Finally, the experimental results showed that compared with the DETR, the accuracy of the IDETR-based worker localization model improved by 2.29%. The accuracy of the GnConv-based YOLOv11 framework in the electric field shielding clothing detection task reaches 90.40%.

Dapeng Ma, Liang Yang, Kang Chen, Feng Yang, Ao Cui, Rundong Yang, Zhilin Wen and Donghua Zhao. “Recursive Gated Convolution-Based YOLOv11 Framework for Operator Safety Management in Live-Line Work”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.11 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161136

@article{Ma2025,
title = {Recursive Gated Convolution-Based YOLOv11 Framework for Operator Safety Management in Live-Line Work},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161136},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161136},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {11},
author = {Dapeng Ma and Liang Yang and Kang Chen and Feng Yang and Ao Cui and Rundong Yang and Zhilin Wen and Donghua Zhao}
}