Construction and Demolition Waste Object Detection Dataset

Published: 19 December 2022| Version 1 | DOI: 10.17632/24d45pf8wm.1
Contributors:
Demetris Demetriou,
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,
,
,

Description

The Construction and Demolition Waste (CDW) dataset was created to facilitate the development and benchmarking of object detection models responsible for the localization and classification of three common object classes of CDW, namely concrete, brick and tile in CDW sorting facilities, under working conditions. For the development of the training and testing datasets, samples belonging to these three object classes were extracted from manually sorted piles of CDW from a recycling facility in Cyprus and digital images of the material were recorded in a controlled environment on the conveyor belt of a full-scale model sorting platform with the use of a colored (RGB) camera. A total of 550 .JPG mages were recorded (containing approximately 4 samples of each object class) at a resolution of 1920 x 1200 x 3, and a total of approximately 6600 samples of all object categories were attained. We emphasized on the complexity of CDW in working conditions by capturing the heterogeneity, surface contamination, irregularity as well as the adherence and stacking of samples in two independent testing datasets. The first testing dataset (testing_set_1) represents an idealized case of CDW placement where the samples are sparsely spaced on the conveyor belt and serves as a baseline indicator of model performance. The second dataset (testing_set_2) contains heavily stacked and adhered samples of CDW so as to give a more representative evaluation of the models under working conditions. Accordingly, we split the samples in three sets with the following distribution: - Training set: 4230 samples are used for training the object detectors - Testing_set_1 : 1727 samples are used for testing the object detectors on an idealized case of CDW placement - Testing_set_2: 596 samples are used for testing the object detectors on heavily stacked and adhered cases To facilitate implementation, in tandem with the provided images, the dataset contains carefully annotated ground-truth bounding boxes in .xml format.

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Categories

Machine Learning, Deep Learning

Funding

Cyprus Research & Innovation Foundation (RIF)

INTEGRATED/0918/0052

European Regional Development Fund

INTEGRATED/0918/0052

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