Semi-supervised segmentation for construction and demolition waste recognition in-the-wild: Adversarial dual-view networks

Abstract

Precise, and automated segmentation of construction and demolition waste (CDW) is crucial for recognizing the composition of mixed waste streams and facilitating automatic waste sorting. Training a neural network for image segmentation is challenging due to the time and resource-intensive nature of annotating large-scale datasets, particularly for domain-specific waste recognition in cluttered environments. In this paper, we propose a semi-supervised multi-class segmentation approach to recognize CDW in real-world settings, utilizing an adversarial dual-view framework. In doing so, we utilize a critic network to enable mutual learning between views using high-confidence predictions. We collected and annotated images of CDW in-the-wild and experimented with various portions of unlabelled data. By minimizing a multi-task loss function, inclusive of supervised, unsupervised, and adversarial losses, our method achieves a frequency-weighted intersection over union of 0.62 and mean pixel accuracy of 0.76 across eight classes, with equal splits of labelled and unlabelled data. The findings realize the proposed method achieves competitive performance compared to fully supervised methods even with limited labelled data. This is useful in waste recognition practices by reducing the time and resources needed for data annotations. Furthermore, it paves the way for accurate waste sorting, facilitating efficient CDW recycling and resource recovery.