Boilers, as core and critical equipment in energy, chemical, and other fields, have water-cooled wall tubes that operate under harsh conditions of high temperature, high pressure, and corrosive media for extended periods. These tubes are prone to corrosion failure, directly threatening equipment safety and causing significant economic losses. Current corrosion detection of boiler water-cooled wall tubes largely relies on manual visual inspection or traditional non-destructive testing methods, which suffer from low efficiency, high subjectivity, insufficient accuracy, and difficulty in adapting to large-scale testing needs. To address these challenges, this paper proposes a precise corrosion image detection scheme based on computer vision big data algorithms, constructing a complete technical system encompassing "data acquisition and preprocessing - feature extraction and fusion - corrosion identification and quantification." First, a multi-source image acquisition platform is built to obtain a large-scale dataset of water-cooled wall tube corrosion images, and data augmentation and denoising algorithms are used to improve data quality. Second, an improved convolutional neural network and attention mechanism are integrated to construct a feature extraction model, achieving accurate capture of deep semantic features and local detail features of the corrosion area. Finally, a multi-scale corrosion quantification assessment module is designed to complete corrosion level classification and damage degree quantification. Experimental results show that the proposed computer vision big data algorithm achieves the highest detection accuracy on both self-built datasets and publicly available industrial datasets, reaching 96.2% and 93.5% respectively. Compared with the ResNet-50 algorithm, the accuracy is improved by 10.4 percentage points and 12.3 percentage points respectively. It can effectively meet the needs of accurate detection of corrosion of boiler water-cooled wall tubes in industrial scenarios and provide reliable technical support for equipment life cycle management.
Zhang et al. (Thu,) studied this question.