  Implementing precise detection of oil leaks in peak load equipment through
image analysis can significantly enhance inspection quality and ensure the
system's safety and reliability. However, challenges such as varying shapes of
oil-stained regions, background noise, and fluctuating lighting conditions
complicate the detection process. To address this, the integration of logical
rule-based discrimination into image recognition has been proposed. This
approach involves recognizing the spatial relationships among objects to
semantically segment images of oil spills using a Mask RCNN network. The
process begins with histogram equalization to enhance the original image,
followed by the use of Mask RCNN to identify the preliminary positions and
outlines of oil tanks, the ground, and areas of potential oil contamination.
Subsequent to this identification, the spatial relationships between these
objects are analyzed. Logical rules are then applied to ascertain whether the
suspected areas are indeed oil spills. This method's effectiveness has been
confirmed by testing on images captured from peak power equipment in the field.
The results indicate that this approach can adeptly tackle the challenges in
identifying oil-contaminated areas, showing a substantial improvement in
accuracy compared to existing methods.
