Wanrun Li^1,2,3,*, Wenhai Zhao¹, Tongtong Wang¹, Yongfeng Du^1,2,3

Structural Durability & Health Monitoring, Vol.18, No.5, pp. 553-575, 2024, DOI:10.32604/sdhm.2024.050751 - 19 July 2024

Abstract The accumulation of defects on wind turbine blade surfaces can lead to irreversible damage, impacting the aerodynamic performance of the blades. To address the challenge of detecting and quantifying surface defects on wind turbine blades, a blade surface defect detection and quantification method based on an improved Deeplabv3+ deep learning model is proposed. Firstly, an improved method for wind turbine blade surface defect detection, utilizing Mobilenetv2 as the backbone feature extraction network, is proposed based on an original Deeplabv3+ deep learning model to address the issue of limited robustness. Secondly, through integrating the concept of… More > Graphic Abstract

Qixiang Tong, Zhipeng Zhu, Min Zhang, Kerui Cao, Haihua Xing^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1353-1375, 2024, DOI:10.32604/cmc.2024.049187 - 25 April 2024

Abstract High-resolution remote sensing image segmentation is a challenging task. In urban remote sensing, the presence of occlusions and shadows often results in blurred or invisible object boundaries, thereby increasing the difficulty of segmentation. In this paper, an improved network with a cross-region self-attention mechanism for multi-scale features based on DeepLabv3+ is designed to address the difficulties of small object segmentation and blurred target edge segmentation. First, we use CrossFormer as the backbone feature extraction network to achieve the interaction between large- and small-scale features, and establish self-attention associations between features at both large and small… More >

Jianwei Yuan¹, Xinli Song^1,*, Huaijian Pu², Zhixiong Zheng³, Ziyang Niu³

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6485-6503, 2023, DOI:10.32604/cmc.2023.035165 - 28 December 2022

Abstract Regular inspection of bridge cracks is crucial to bridge maintenance and repair. The traditional manual crack detection methods are time-consuming, dangerous and subjective. At the same time, for the existing mainstream vision-based automatic crack detection algorithms, it is challenging to detect fine cracks and balance the detection accuracy and speed. Therefore, this paper proposes a new bridge crack segmentation method based on parallel attention mechanism and multi-scale features fusion on top of the DeeplabV3+ network framework. First, the improved lightweight MobileNet-v2 network and dilated separable convolution are integrated into the original DeeplabV3+ network to improve… More >

Javaria Amin¹, Muhammad Sharif², Muhammad Almas Anjum³, Yunyoung Nam^4,*, Seifedine Kadry⁵, David Taniar⁶

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2451-2467, 2021, DOI:10.32604/cmc.2021.014199 - 13 April 2021

Abstract Coronavirus 19 (COVID-19) can cause severe pneumonia that may be fatal. Correct diagnosis is essential. Computed tomography (CT) usefully detects symptoms of COVID-19 infection. In this retrospective study, we present an improved framework for detection of COVID-19 infection on CT images; the steps include pre-processing, segmentation, feature extraction/fusion/selection, and classification. In the pre-processing phase, a Gabor wavelet filter is applied to enhance image intensities. A marker-based, watershed controlled approach with thresholding is used to isolate the lung region. In the segmentation phase, COVID-19 lesions are segmented using an encoder-/decoder-based deep learning model in which deepLabv3… More >