Crude Oil Leakage Detection Based on DA‐SR Framework. Issue 9 (1st July 2022)

• Record Type: Journal Article
• Authors: Gong, Faming; Gao, Yating; Yuan, Xiangbing; Liu, Xin; Li, Yunjing; Ji, Xiaofeng
• Is Part Of: Advanced theory and simulations. Volume 5:Issue 9(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-07-01
• Subjects: crude oil leakage detection -- data augmentation -- object detection -- shadow removal -- YOLOv4; Science -- Simulation methods -- Periodicals; Science -- Methodology -- Periodicals; Engineering -- Simulation methods -- Periodicals; Engineering -- Methodology -- Periodicals; 507.21
• DOI: 10.1002/adts.202200273
• Languages: English
• ISSNs: 2513-0390

Abstract

Abstract: Crude oil leakage is a security issue that needs to be avoided in many production areas such as oil fields and substations. However, crude oil leakage image data is often difficult to obtain due to security and privacy issues in the working area. And shadow interference is also a challenge for oil leakage detection tasks. This paper proposes a crude oil leakage detection method based on the DA‐SR framework. The framework consists of two parts: the data augmentation module and shadow removal module. High‐quality synthetic oil leakage images are generated using the cycle‐consistent adversarial networks (CycleGAN), and further process the synthetic images by a T‐CutMix sample processing method. To solve the problem of shadow interference, this paper uses the FlocalLoss function to calculate the confidence loss based on the YOLOv4 detection network and a hard sample retraining (HSR) algorithm to enhance the images with shadows. The experiments demonstrate that the combination of original and synthetic images when training the model can improve the performance of oil leakage detection. Finally, it is also shown that the detector built from the framework can effectively reduce the false detection of shadows. Abstract : The high similarity between the shadow image and the oil leakage image is used to synthesize new oil leakage images by means of CycleGAN. The diversity of samples is further refined using T‐CutMix. The FlocalLoss function and HSR algorithm are investigated to overcome the interference of shadows on the detection model