Textile and colour defect detection using deep learning methods
Abstract Recent advances in deep learning (DL) have significantly enhanced the detection of textile and colour defects. This review focuses specifically on the application of DL‐based methods for defect detection in textile and coloration processes, with an emphasis on object detection and related computer vision (CV) tasks.
Hao Cui +2 more
wiley +1 more source
Evaluating Population Normalization Methods Using Chemical Data for Wastewater-Based Epidemiology: Insights from a Site-Specific Case Study. [PDF]
Verani M +8 more
europepmc +1 more source
Fate, occurrence, and regional-scale emissions of neonicotinoid pesticides and their metabolites in wastewater treatment plants in suburban Shanghai, China. [PDF]
Zhang Y +8 more
europepmc +1 more source
Moriah, Town of and Town of Moriah Highway Department Unit, International Brotherhood of Teamsters (IBT), Local 294 (2016) [PDF]
core +1 more source
Dataset on wastewater quality monitoring with adsorption and reflectance spectrometry in the UV-vis range. [PDF]
Lechevallier P +7 more
europepmc +1 more source
Container-based sanitation in Nairobi's Mukuru: roles, power, challenges and strategies. [PDF]
Chumo I, Karani C, Riungu J, Kirimi LM.
europepmc +1 more source
Modeling and Evaluating Integrated Pollution Control Measures in Rivers: A Case Study of the Lianjiang River Basin. [PDF]
Zheng J +7 more
europepmc +1 more source

