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.

翻译：通过图像分析实现对调峰设备渗油区域的精确检测，可显著提升巡检质量并保障系统安全可靠性。然而，油污区域形态多变、背景噪声干扰及光照条件波动等挑战增加了检测难度。为此，本文提出将基于逻辑规则的判别融入图像识别的方法。该方法通过分析空间对象间的语义关系，利用Mask RCNN网络对溢油图像进行语义分割。首先采用直方图均衡化增强原始图像，进而使用Mask RCNN识别油罐、地面及潜在油污区域的初始位置与轮廓。在此基础上，分析各对象间的空间关系，并应用逻辑规则判定疑似区域是否为真实溢油。通过现场调峰设备图像的测试验证，该方法能有效应对油污区域识别中的挑战，相较现有方法显著提升了识别准确率。