This paper presents advanced intelligent monitoring methods aimed at enhancing
the quality and durability of asphalt pavement construction. The study focuses
on two critical tasks: foreign object detection and the uniform application of
tack coat oil. For object recognition, the YOLOv5 algorithm is employed, which
provides real-time detection capabilities essential for construction
environments where timely decisions are crucial. A meticulously annotated
dataset comprising 4,108 images, created with the LabelImg tool, ensures the
accurate detection of foreign objects such as leaves and cigarette butts. By
utilizing pre-trained weights during model training, the research achieved
significant improvements in key performance metrics, including precision and
recall rates.
In addition to object detection, the study explores color space analysis through
the HSV (Hue, Saturation, Value) model to effectively differentiate between
coated and uncoated pavement areas following the application of tack coat oil.
Statistical analyses, including mean and standard deviation calculations of HSV
values, provide critical insights into color differences that inform the
establishment of threshold settings for effective identification. The research
also addresses various challenges posed by environmental factors, such as steam
and smoke, which can interfere with visual recognition during construction
operations. To mitigate these challenges, an innovative automated mechanical
system was designed to stabilize the camera, ensuring consistent data
acquisition and significantly enhancing the reliability of visual data for
detection tasks. By significantly improving identification accuracy and overall
pavement quality, this research contributes to the development of more efficient
construction methodologies and maintenance procedures. The implications of this
work suggest that the adoption of advanced technologies is vital for
facilitating reliable and efficient construction processes, ultimately leading
to better long-term performance of asphalt pavement surfaces. This study aims to
establish a foundation for future research in intelligent monitoring, promoting
the continuous improvement of construction practices within the industry.