ConstScene: Dataset and Model for Advancing Robust Semantic Segmentation in Construction Environments
CoRR(2023)
摘要
The increasing demand for autonomous machines in construction environments
necessitates the development of robust object detection algorithms that can
perform effectively across various weather and environmental conditions. This
paper introduces a new semantic segmentation dataset specifically tailored for
construction sites, taking into account the diverse challenges posed by adverse
weather and environmental conditions. The dataset is designed to enhance the
training and evaluation of object detection models, fostering their
adaptability and reliability in real-world construction applications. Our
dataset comprises annotated images captured under a wide range of different
weather conditions, including but not limited to sunny days, rainy periods,
foggy atmospheres, and low-light situations. Additionally, environmental
factors such as the existence of dirt/mud on the camera lens are integrated
into the dataset through actual captures and synthetic generation to simulate
the complex conditions prevalent in construction sites. We also generate
synthetic images of the annotations including precise semantic segmentation
masks for various objects commonly found in construction environments, such as
wheel loader machines, personnel, cars, and structural elements. To demonstrate
the dataset's utility, we evaluate state-of-the-art object detection algorithms
on our proposed benchmark. The results highlight the dataset's success in
adversarial training models across diverse conditions, showcasing its efficacy
compared to existing datasets that lack such environmental variability.
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