Research on Transmission Line Personal Protective Equipment Detection Algorithm Based on Improved YOLOv11

Summary

The goal of this protocol is to provide a step-by-step guide for developing an improved YOLOv11n-based model for PPE detection. It details architectural and training modifications that result in a lightweight, real-time detector with a state-of-the-art accuracy of 90.1% mAP and inherent interpretability.

Abstract

In intelligent transmission line inspection, edge computing devices face the critical challenge of balancing real-time performance with detection accuracy for personal protective equipment recognition in complex operational scenarios. This study proposes WTLS-YOLOv11n, a lightweight and inherently interpretable detection algorithm. The methodology integrates three synergistic innovations into the YOLOv11n architecture. A C3K2-WTConv module employing a discrete wavelet transform decomposes features into physically meaningful frequency components, enabling robust multi-scale feature extraction with inherent interpretability. A lightweight shared composite detection head achieves substantial parameter reduction through strategic weight sharing while preserving multi-scale fusion capabilities. The MPDIoU loss function enhances localization accuracy for small and irregularly shaped targets. Experimental validation demonstrates that the proposed model achieves superior detection accuracy with significantly reduced parameters and computational complexity compared to the baseline, while maintaining real-time inference performance on edge hardware platforms. Quantitative interpretability analysis reveals that detection decisions are predominantly driven by low-frequency structural features rather than high-frequency textural details, providing transparent insight into the model's reasoning process. Comparative experiments against mainstream detection models validate the superior accuracy-efficiency trade-off of the proposed approach. This work establishes a transparent, efficient, and trustworthy solution for automated safety supervision in electrical power operations, with broader applicability to safety-critical detection tasks requiring edge deployment and interpretable decision-making.

Introduction

Deep learning has transformed industrial safety protocols and introduced new approaches to occupational risk management. This transformation is particularly evident in high-risk sectors such as power systems engineering. Vision-based intelligent supervision systems now leverage the YOLO (You Only Look Once)¹ architecture to enable automated detection of Personal Protective Equipment (PPE)^2,3 in real time. These systems have emerged as a critical technology for workplace safety monitoring, offering significant improvements in detection accuracy and response speed compared to traditional ....

Protocol

The image data for this study were collected from power operation environments with necessary permissions. All images were anonymized with no personally identifiable information retained. This study focuses on detecting personal protective equipment rather than identifying individuals. As the research involves only algorithm development using anonymized data, ethical approval was not required.

The following protocol details a comprehensive procedure for designing, training, and evaluating a lightweight, high-performance, and inherently interpretable object detection model, named WTLS-YOLOv11n, for Personal Protective Equipment (PPE) detecti....

Results

To evaluate the performance of the proposed WTLS-YOLOv11n model, we conducted experiments on our self-built PPE dataset and the PASCAL VOC 2007+2012 dataset. All metrics are reported on the respective test sets unless otherwise stated.

Comparison with State-of-the-Art Models

We benchmarked WTLS-YOLOv11n against mainstream object detectors across three architectural paradigms: CNN-based YOLO models (YOLOv5n, YOLOv8n, YOLOv9t, YOLOv11n), a two-stage .......

Discussion

The experimental results demonstrate that WTLS-YOLOv11n achieves 90.1% mAP while reducing parameters by 11.5% compared to the YOLOv11n baseline. This improvement in both accuracy and efficiency stems from the synergistic integration of three components: wavelet-based feature extraction, lightweight detection head design, and improved localization loss.

The ablation study reveals that the C3K2-WTConv module provides the largest individual performance gain, increasing mAP from 86.3% to 89.1%. Th.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
CUDA Toolkit	NVIDIA Corporation	CUDA Toolkit Version 11.7
Deep Learning Framework	PyTorch Foundation	PyTorch Version 1.12.0
Graphics Processing Unit	NVIDIA Corporation	GeForce RTX 4090
NumPy	NumPy Developers	NumPy
OpenCV	OpenCV Team	opencv-python
Operating System	Canonical Ltd.	Ubuntu 22.04 LTS (or specify your OS)
Python	Python Software Foundation	Python Version 3.8 or higher
TensorBoard	TensorFlow Authors	TensorBoard
Torchvision	PyTorch Foundation	Torchvision