Multi-channel input augmentation for sonar image–based recognition of drowned victims

Sonar imaging provides representative real-time mapping of the water bottom, which is particularly valuable in search-and-recovery operations, as it helps quickly locate drowned victims. However, practical analysis of sonar data is often challenging due to its complexity and automated recognition demanding substantial training data. In this paper, we present an extension to SDVD (Sonar Drowned Victims Dataset), a sonar image dataset with annotated binary masks marking drowned victims for training segmentation models. To improve segmentation processing, we propose Multi-Channel Input Augmentation (MCIA). This method augments the segmented image with additional channels, increasing it from one to seven, thereby enhancing the model's information input. To achieve this, each channel represents a processed original image by applying: Sobel edge detection, Haar wavelet, High-Pass features, pixelated map, CLAHE and distance transform. To evaluate segmentation performance, we tested different models on all SDVD subsets, alongside various data augmentation techniques, such as RICAP. Among the models, MCIA-U²Net demonstrated the highest accuracy, achieving an Intersection over Union (IoU) of 75.89% and a Dice Score of 86.30% on base SDVD, 71.57% IoU and 83.43% Dice Score on CleanedSDVD, and 68.55% IoU and 81.33% Dice Score on ExtendedSDVD. Additionally, we utilized a channel-level Local Interpretable Model-Agnostic Explanation (LIME) method for detailed evaluation underlining the efficiency of the proposal. These results highlight the effectiveness of the proposed approach in training segmentation models and indicate significant potential for further enhancements in sonar image recognition methods for victim recovery applications.