Automatic Identification of Sunken Oil in Homogeneous Information Perturbed Environment through Fusion Image Enhancement with Convolutional Neural Network

Jinfeng Cao (), Mingzhong Gao (), Jihong Guo, Haichun Hao, Yongjun Zhang, Peng Liu and Nan Li
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Jinfeng Cao: School of Mechanical & Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China
Mingzhong Gao: Guangdong Provincial Key Laboratory of Deep Earth Sciences and Geothermal Energy Exploitation and Utilization, Institute of Deep Earth Sciences and Green Energy, College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China
Jihong Guo: School of Energy and Mining Engineering, China University of Mining & Technology, Beijing 100083, China
Haichun Hao: Guangdong Provincial Key Laboratory of Deep Earth Sciences and Geothermal Energy Exploitation and Utilization, Institute of Deep Earth Sciences and Green Energy, College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China
Yongjun Zhang: School of Mechanical & Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China
Peng Liu: School of Mechanical & Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China
Nan Li: School of Mechanical & Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China

Sustainability, 2024, vol. 16, issue 15, 1-16

Abstract: With the development of the marine oil industry, leakage accidents are one of the most serious problems threatening maritime and national security. The spilt crude oil can float and sink in the water column, posing a serious long-term threat to the marine environment. High-frequency sonar detection is currently the most efficient method for identifying sunken oil. However, due to the complicated environment of the deep seabed and the interference of the sunken oil signals with homogeneous information, sonar detection data are usually difficult to interpret, resulting in low efficiency and a high failure rate. Previous works have focused on features designed by experts according to the detection environments and the identification of sunken oil targets regardless of the feature extraction step. To automatically identify sunken oil targets without a prior knowledge of the complex seabed conditions during the image acquisition process for sonar detection, a systematic framework is contrived for identifying sunken oil targets that combines image enhancement with a convolutional neural network (CNN) classifier for the final decision on sunken oil targets examined in this work. Case studies are conducted using datasets obtained from a sunken oil release experiment in an outdoor water basin. The experimental results show that (i) the method can effectively distinguish between the sunken oil target, the background, and the interference target; (ii) it achieved an identification accuracy of 83.33%, outperforming feature-based recognition systems, including SVM; and (iii) it provides important information about sunken oil such as the location of the leak, which is useful for decision-making in emergency response to oil spills at sea. This line of research offers a more robust and, above all, more objective option for the difficult task of automatically identifying sunken oils under complex seabed conditions.

Keywords: identification of sunken oil; homogeneous information; convolutional neural network; sonar detection (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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