An ensemble method for automatic real-time detection, evaluation and position of exposed subsea pipelines based on 3D real-time sonar system

Evaluating the Adaptability of Deep Learning-Based Multi-feature Sonar Image Detection Algorithms

“…Abbas and Celebi [10] developed a new dermal classification system by integrating multiple visual features and deep neural networks, which can extract new aggregates of visual features and descriptors in perceptual color spaces. To achieve efficient and accurate monitoring of subsea pipelines, Xiong et al [11] proposed a real-time automatic monitoring, evaluation, and positioning integrated subsea pipeline status monitoring and evaluation method based on 3D real-time sonar. This method can improve the real-time monitoring of subsea pipelines, avoid manual intervention, and ensure the efficiency and accuracy of subsea pipeline status monitoring.…”

Revealing the Potential of Deep Learning for Detecting Submarine Pipelines in Side-Scan Sonar Images: An Investigation of Pre-Training Datasets

“…While these areas are important, they are notably less complex in terms of the variety and nuances of the data involved compared to POC detection. With advancements in technology, researchers began applying CNNs to more complex tasks, such as underwater wreck detection [25,26], the real-time processing of side-scan sonar data [27], and developing novel models for SSS image recognition such as U-Net [28] and VIT [29]. The focus of our work was to address the limited availability and quality of training data, a problem that was not adequately addressed in previous studies.…”