Defects in the textile manufacturing process lead to a great waste of resources and further affect the quality of textile products. Automated quality guarantee of textile fabric materials is one of the most important and demanding computer vision tasks in textile smart manufacturing. This survey presents a thorough overview of algorithms for fabric defect detection. First, this review briefly introduces the importance and inevitability of fabric defect detection towards the era of manufacturing of artificial intelligence. Second, defect detection methods are categorized into traditional algorithms and learning-based algorithms, and traditional algorithms are further categorized into statistical, structural, spectral, and model-based algorithms. The learning-based algorithms are further divided into conventional machine learning algorithms and deep learning algorithms which are very popular recently. A systematic literature review on these methods is present. Thirdly, the deployments of fabric defect detection algorithms are discussed in this study. This paper provides a reference for researchers and engineers on fabric defect detection in textile manufacturing.

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Purpose This study aims to analyze reform path for waste management policy implementation. With reference to the Bayesian theory, this study provides a dynamic policy conversion method through various context settings. Furthermore, this study attempts to present an empirical research paradigm. Design/methodology/approach Matland’s “ambiguity-conflict model” is applied to explain the problems and reform paths of China’s waste management policy implementation. Integrating structure discovery and bibliometrics into qualitative analysis, this study used search data from literature search engine with specific themes to achieve structure learning of Bayesian network with key factors refined in waste management policy. Findings The results show that China’s waste management policy implementation belongs to symbolic implementation with high ambiguity and high conflict. Four basic conversion paths for the waste management policy are proposed, which are classified by length and stability. Then, it is possible to locate the factors, paths and types of policy implementation through involvement analysis with features of each path and each district of policy implementation. Public education holds direct but unstable impact on waste management. Economic incentives hold continuous but gradually diminishing impact. Perceived policy effectiveness plays the crucial role like a central bridge. Resident conditions have a positive impact, which could be enhanced through economic development of China. The impact of subjective norm on waste management is not significant. But subjective norm has the potential breakthrough for solving stagnation of waste classification policy. However, the impacts from each factor may change along with economy growth and technology innovation. Originality/value This study uses the “ambiguity-conflict model” to position China’s waste classification policy and suggests that structure discovery methods help understand feasible reform paths for reform policy. The integration of theoretical analysis and quantitative simulation can achieve a comprehensive analysis of problems and solutions in waste management policy implementation of China. Promotion and education, economic incentives, perceived value, behavior control, subjective norm, perceived policy effectiveness, informal waste recycling system and residential conditions are explored as key factors for waste classification policy implementation as a representative in waste management policy. The role of each key factor and features of each conversion paths are integrated to position reform paths in the ambiguity-conflict model. This work helps to explain the stagnation of waste management policy implementation from the perspective of dynamic structure evolution, and some specific suggestions to get out of stagnation are proposed.