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.

通过图像分析准确检测峰值负载设备中的油泄漏，可以显著提高检查质量，确保系统的安全性和可靠性。该方法通过将逻辑规则辨别与图像识别集成，整合了 Mask RCNN 网络的语义图像分割技术，以识别潜在的油污染区域，并应用逻辑规则来确定疑似区域是否为油泄漏，通过测试表明，与现有方法相比，该方法在准确识别油污染区域方面取得了显著的改进。