YOLO-HVConv: A Horizontally-Vertically Aware YOLO for Road Damage Detection

Road damage detection is an important component
of the intelligent transportation system, which helps in timely
maintenance of roads and enhances transportation safety. How
ever, accurate detection of road damages is still a challenge due
to the presence of small, thin and elongated crack structures,
different damage scales and class imbalance. To address these
issues, this paper proposes an enhanced YOLOv8s-based frame
work for road damage detection by integrating the Horizontal
and Vertical Convolution (HVConv) modules at multiple stages
of feature fusion in the neck. To better model thin and elongated
road damage patterns, we incorporate HVConv into the P3 and
P4 feature levels in the proposed architecture while maintaining
the lightweight property of the original detector. An experiment
was carried out on a combined dataset of the China Drone and
China MotorBike subsets of the RDD2022 dataset. Comparative
evaluations were conducted against the baseline YOLOv8s model
and other HVConv configurations. The proposed YOLO-HVConv
model obtained the best overall performance (i.e., 82.57% preci
sion, 79.89% recall, 83.12% mAP@50, and 51.88% mAP@50:95).
Moreover, the model was more effective in detecting small
and medium road damages, with AP values of 55.71% and
40.57% respectively. The experimental results demonstrate that
the suggested integration method of multi-scale HVConv can
enhance the feature representation for road damage detection
while maintaining computational efficiency, making it suitable
for practical intelligent road monitoring applications.