Chen-Chiung Hsieh^1,*, Chun-An Chen¹, Wei-Hsin Huang²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072514 - 12 January 2026

Abstract Container transportation is pivotal in global trade due to its efficiency, safety, and cost-effectiveness. However, structural defects—particularly in grapple slots—can result in cargo damage, financial loss, and elevated safety risks, including container drops during lifting operations. Timely and accurate inspection before and after transit is therefore essential. Traditional inspection methods rely heavily on manual observation of internal and external surfaces, which are time-consuming, resource-intensive, and prone to subjective errors. Container roofs pose additional challenges due to limited visibility, while grapple slots are especially vulnerable to wear from frequent use. This study proposes a two-stage automated… More >

Yu Chen¹, Sagar A. S. M. Sharifuzzaman², Hangxiang Wang¹, Yanfen Li¹, L. Minh Dang³, Hyoung-Kyu Song³, Hyeonjoon Moon^1,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5455-5473, 2023, DOI:10.32604/cmc.2023.033787 - 29 April 2023

Abstract The sewer system plays an important role in protecting rainfall and treating urban wastewater. Due to the harsh internal environment and complex structure of the sewer, it is difficult to monitor the sewer system. Researchers are developing different methods, such as the Internet of Things and Artificial Intelligence, to monitor and detect the faults in the sewer system. Deep learning is a promising artificial intelligence technology that can effectively identify and classify different sewer system defects. However, the existing deep learning based solution does not provide high accuracy prediction and the defect class considered for… More >

Wuqin Tang, Qiang Yang, Wenjun Yan^*

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1423-1439, 2022, DOI:10.32604/cmes.2022.018313 - 30 December 2021

Abstract Defects detection with Electroluminescence (EL) image for photovoltaic (PV) module has become a standard test procedure during the process of production, installation, and operation of solar modules. There are some typical defects types, such as crack, finger interruption, that can be recognized with high accuracy. However, due to the complexity of EL images and the limitation of the dataset, it is hard to label all types of defects during the inspection process. The unknown or unlabeled create significant difficulties in the practical application of the automatic defects detection technique. To address the problem, we proposed… More >

Jakkrit Onshaunjit, Jakkree Srinonchat^*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 355-367, 2022, DOI:10.32604/cmc.2022.017698 - 03 November 2021

Abstract An ideal printed circuit board (PCB) defect inspection system can detect defects and classify PCB defect types. Existing defect inspection technologies can identify defects but fail to classify all PCB defect types. This research thus proposes an algorithmic scheme that can detect and categorize all 14-known PCB defect types. In the proposed algorithmic scheme, fuzzy c-means clustering is used for image segmentation via image subtraction prior to defect detection. Arithmetic and logic operations, the circle hough transform (CHT), morphological reconstruction (MR), and connected component labeling (CCL) are used in defect classification. The algorithmic scheme achieves More >