Image Recognition of Oil Leakage Area Based on Logical Semantic Discrimination

• URL: http://arxiv.org/abs/2311.02256v2
• Date: Fri, 17 Nov 2023 06:58:21 GMT
• Title: Image Recognition of Oil Leakage Area Based on Logical Semantic Discrimination
• Authors: Weiying Lin, Che Liu, Xin Zhang, Zhen Wei, Sizhe Li, Xun Ma
• Abstract summary: 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. The results indicate that this approach can adeptly tackle the challenges in identifying oil-contaminated areas.
• Score: 11.792559057876693
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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