<p>Cracks
considerably reduce the life span of pavement surfaces. Currently, there is a need
for the development of robust automated distress evaluation systems that comprise
a low-cost crack detection method for performing fast and cost-effective
roadway health monitoring practices. Most of the current methods are costly and
have labor-intensive learning processes, so they are not suitable for small
local-level projects with limited resources or are only usable for specific
pavement types.</p>
<p>This
paper proposes a new method that uses an improved version of the weighted
neighborhood pixels segmentation algorithm to detect cracks in 2-D pavement
images. This method uses the Gaussian cumulative density function as the
adaptive threshold to overcome the drawback of fixed thresholds in noisy
environments. The proposed algorithm was tested on 300 images containing a wide
range of noise representative of different noise conditions. This method proved
to be time and cost-efficient as it took less than 3.15 seconds per 320 × 480
pixels image for a Xeon (R) 3.70 GHz CPU processor to determine the detection
results. This makes the model a perfect choice for county-level pavement maintenance
projects requiring cost-effective pavement crack detection systems. The
validation results were promising for the detection of low to severe-level
cracks (Accuracy = 97.3%, Precision = 79.21%, Recall= 89.18% and F<sub>1</sub>
score = 83.9%).</p>
A Novel Adaptive Pixels Segmentation Algorithm for Pavement Crack Detection