Underwater Target Detection Based on Single-Photon Imaging: Noise Model-Driven Data Augmentation and Deep Learning Network Training

With the expansion of marine development and the occurrence of underwater accidents, the strategic value and practical urgency of searching for wreckage, such as shipwrecks and aircraft, and marine emergency rescue missions continue to rise. However, traditional underwater detection methods have inherent limitations in weak signal resolution and positioning accuracy, severely hindering the accurate identification of underwater debris and the efficiency of emergency responses. To address this issue, an underwater target detection system based on singlephoton imaging technology is presented, employing a technical approach that integrates a noise model-driven data augmentation strategy with deep learning network training. By constructing a multidimensional noise coupling model, integrating typical underwater noise characteristics such as photon shot noise and water scattering noise, a highly realistic virtual underwater singlephoton echo dataset is synthesized, providing standardized data support for model training and testing. Furthermore, by deeply integrating the virtual dataset with the target's physical geometric characteristics, such as debris outline and scale parameters, highprecision detection of underwater debris is achieved. Through the detection of wreckage, the test results show that the recognition accuracy of shipwrecks and aircraft wreckage reaches 0.67, indicating that this method can effectively address engineering problems such as the high cost of acquiring underwater target detection data, insufficient sample diversity, and low efficiency of underwater wreckage detection and emergency rescue.