Deep learning-based waste detection in natural and urban environments. (1st February 2022)

• Record Type: Journal Article
• Authors: Majchrowska, Sylwia; Mikołajczyk, Agnieszka; Ferlin, Maria; Klawikowska, Zuzanna; Plantykow, Marta A.; Kwasigroch, Arkadiusz; Majek, Karol
• Is Part Of: Waste management. Volume 138(2022)
• Page Start: 274
• Page End: 284
• Publication Date: 2022-02-01
• Subjects: Object detection -- Semi-supervised learning -- Waste classification benchmarks -- Waste detection benchmarks -- Waste localization -- Waste recognition; Hazardous wastes -- Periodicals; Refuse and refuse disposal -- Periodicals; 363.728
• DOI: 10.1016/j.wasman.2021.12.001
• Languages: English
• ISSNs: 0956-053X

Abstract

Highlights: Detect-waste and classify-waste datasets benchmarks were introduced. The quality of waste detection in various environments has been tested. Semi-supervised training can boost classification of different types of waste. Remarkable performance in waste detection is obtained by the two-stage framework. Abstract: Waste pollution is one of the most significant environmental issues in the modern world. The importance of recycling is well known, both for economic and ecological reasons, and the industry demands high efficiency. Current studies towards automatic waste detection are hardly comparable due to the lack of benchmarks and widely accepted standards regarding the used metrics and data. Those problems are addressed in this article by providing a critical analysis of over ten existing waste datasets and a brief but constructive review of the existing Deep Learning-based waste detection approaches. This article collects and summarizes previous studies and provides the results of authors' experiments on the presented datasets, all intended to create a first replicable baseline for litter detection. Moreover, new benchmark datasets detect-waste and classify-waste are proposed that are merged collections from the above-mentioned open-source datasets with unified annotations covering all possible waste categories: bio, glass, metal and plastic, non-recyclable, other, paper, and unknown . Finally, a two-stage detector for litter localization and classification is presented. EfficientDet-D2 is used to localize litter, and EfficientNet-B2 to classify the detected waste into seven categories. The classifier is trained in a semi-supervised fashion making the use of unlabeled images. The proposed approach achieves up to 70% of average precision in waste detection and around 75% of classification accuracy on the test dataset. The code and annotations used in the studies are publicly available online 1 .