  The Pavement Condition Index (PCI) is a widely used metric for evaluating
pavement performance based on the type, extent and severity of distresses
detected on a pavement surface. In recent times, significant progress has been
made in utilizing deep-learning approaches to automate PCI estimation process.
However, the current approaches rely on at least two separate models to
estimate PCI values -- one model dedicated to determining the type and extent
and another for estimating their severity. This approach presents several
challenges, including complexities, high computational resource demands, and
maintenance burdens that necessitate careful consideration and resolution. To
overcome these challenges, the current study develops a unified multi-tasking
model that predicts the PCI directly from a top-down pavement image. The
proposed architecture is a multi-task model composed of one encoder for feature
extraction and four decoders to handle specific tasks: two detection heads, one
segmentation head and one PCI estimation head. By multitasking, we are able to
extract features from the detection and segmentation heads for automatically
estimating the PCI directly from the images. The model performs very well on
our benchmarked and open pavement distress dataset that is annotated for
multitask learning (the first of its kind). To our best knowledge, this is the
first work that can estimate PCI directly from an image at real time speeds
while maintaining excellent accuracy on all related tasks for crack detection
and segmentation.
