Operational Assessment of Side-Scan Sonar Data Applied to Naval Mine Detection Using an Automatic Target Recognition Algorithm

Mine warfare (MW) and mine countermeasures (MCMs) have become strategic options to ensure national sovereignty and the safety of maritime commercial routes, which is the primary logistics system for international trade. As an asymmetric weapon, locating and neutralizing a naval mine poses a significant challenge for the world’s navies. In this context, this work proposes an object detection model based on you only look once, version 11 (YOLOv11) for automatic and real-time detection of naval mines in harbor areas using side-scan sonar (SSS) data. The main objective of this tool is to apply it to unmanned maritime vehicles (UMVs) to enhance the mine detection efficiency during minehunting operations. Second, this study aims to evaluate the effects of operational parameters, oceanographic and meteorological conditions on the SSS data quality for naval mine detection. All the data used to train the neural network were real and obtained in a test area, mimicking a port area, a strategic environment in the context of MW. The model performed with satisfactory statistical results (mAP@0.5: 0.84, P: 0.93, R: 0.83, and F1 score: 0.88). Based on the results provided in this study, the 0.70 confidence level can be safely used in future operational inferences using this customized model. From the operational evaluation of SSS data quality, the ideal condition for data acquisition is using an intermediary range and high-frequency sonars with calm seas and low speeds. Despite the recent advancements in the field of machine learning, it is unlikely that neural networks will fully replace human operators in MCM missions in the short to medium term. However, they serve as a valuable tool for decision support, enabling rapid analysis of large datasets and filtering information to present only the most relevant data to human analysts, such as potential sea mines. When embedded in UMV, this technology mitigates risks to human life and enables operators to focus on verifying real targets, thereby enhancing the effectiveness of MCM operations.