Automatic classification of textile visual pollutants using deep learning networks

Urban pollution is a massive global problem, especially in industrialized and developing nations. Visual pollution is an issue concerned with the external noticeable appearance of the mod-ern urban areas causing human health disorders, emotional distress, driving distraction, environ-mental hazards, etc. Amidst the plethora of different forms of environmental pollution, visual pollution deteriorates the aesthetics of an urban environment, endorsing the importance of research and assessing it from different dimensions. The main objective of this study is to initialize a new concept of automatic identification and classification of visible contaminants related to textile industries using computer vision techniques. In this work, deep learning techniques have been applied for the automatic detection and classification of three categories of textile-based visual pol-lutants, i.e., cloth garbage, advertising billboards and signages, and textile dyeing waste materials. Initially, 1,709 visual pollutants images were obtained through web crawling of search engines. Additionally, 954 images were collected from two local garments factories, roadside vendors and shopping malls of Bangladesh. Next, the dataset was manually annotated by an open-source label-ing tool. Finally, various deep learning techniques, Faster R-CNN, YOLOv5, and EfficientDet, have been used to classify the obtained dataset automatically. The EfficientDet framework achieved the best performance with 97% and 93% training and test accuracies, respectively. The YOLOv5 approach exhibits acceptable precision with a considerably lower number of epochs. The proposed automated classification system is expected to create future visual pollution ratings for the textile industries. Consequently, the corresponding stakeholders (industry owners, government authorities, factory workers, etc.) can introduce regulatory frameworks and control the proliferation of visual pollution. The open-source images obtained by web crawling, locally collected visual pollutants dataset and implementation code of this work are available at: https://github.com/SadiaA-frin163/Textile-Visual-Pollutants-Dataset.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/ 4.0/).

Automated summary

This study aims to achieve automatic identification and classification of visible pollutants in the textile industry using computer vision techniques. By applying deep learning techniques, especially the EfficientDet framework, high accuracies in classification have been achieved. The proposed automated classification system is expected to provide ratings for visual pollution in the textile industry and guide relevant stakeholders in implementing management measures.