# Icing Detection of Wind Turbine Blades Based on an Improved PP-YOLOE Detection Network

## TL;DR

This paper introduces an improved PP-YOLOE detection network for accurately identifying ice on wind turbine blades in cold, humid regions.

## Contribution

The novel integration of CA, ASPP, and PSO for hyperparameter tuning in a detection network for wind turbine blade icing.

## Key findings

- An improved PP-YOLOE network achieved a multiple average precision of 0.94 for wind turbine blade icing detection.
- The PSO algorithm effectively automated hyperparameter tuning, enhancing model performance.
- The model outperformed the original PP-YOLOE across multiple evaluation metrics.

## Abstract

In cold and highly humid regions, wind turbine blades (WTB) are susceptible to icing, which can have a significant impact on the security and economic operation of turbines. Therefore, precise and prompt icing status detection is pivotal for maintaining wind turbine operational normalcy. In this research, an improved PP-YOLOE network is developed for classifying and detecting the icing state of WTB. First, a dataset of WTB icing is constructed based on a wind tunnel laboratory and expanded to improve the generalization of the model. To enhance feature representation, the network architecture was improved by embedding a coordinate attention (CA) mechanism and integrating atrous spatial pyramid pooling (ASPP) to better capture multi-scale contextual information. Moreover, a key innovation of this work is the novel application of a particle swarm optimization (PSO) algorithm to systematically automate hyperparameter tuning. Through ablation experiments and comparative tests, the improved PP-YOLOE network demonstrates superior overall performance on this dataset, achieving a multiple average precision of 0.94. It surpasses the original model across multiple evaluation metrics, indicating a robust and meaningful enhancement. The improved PP-YOLOE network proposed in this study provides a promising and effective method for WTB icing detection. This work provides a paradigm for applying advanced deep learning techniques to enhance intelligent industrial inspection tasks.