Dynamic Evaluation Method for Underwater Small Target Detection Effectiveness

The evaluation of underwater small target detection effectiveness is a core challenge for ensuring maritime security and resource exploitation. Traditional static evaluation methods rely on fixed environmental parameters and single metrics, making it difficult to capture the dynamic adaptability of algorithms in complex and time-varying marine environments. To address this bottleneck, this paper proposes a novel dynamic effectiveness evaluation method based on multi-indicator fusion using Python. An environment-coupled dynamic detection probability model is established, extending classical detection theory to time-varying marine environments. The weighted geometric mean is introduced into the modeling of the Comprehensive Effectiveness Index (CEI). An underwater detection dynamic evaluation system (UDDES) is designed and developed, supporting dynamic environment simulation, parallel testing of multiple algorithms, and multi-dimensional effectiveness visualization analysis. Simulation experimental results demonstrate that the detection probability of AI-Det is improved by approximately 99.8% compared with conventional beamforming (CBF). In dynamic environment stress tests involving sudden sea state deterioration and SNR drops, the mean CEI of AI-enhanced algorithms is increased by 36.5% over CBF, along with a 44.8% improvement in robustness coefficient and a 55.3% reduction in tracking stability error. This study shows that the proposed framework and system effectively overcome the inability of traditional static evaluation to quantify dynamic performance evolution, providing systematic theoretical methods and engineering tools for closed-loop testing, optimal selection, and effectiveness prediction of underwater detection algorithms.