Autonomous underwater vehicles (AUVs) are increasingly important tools in industries ranging from research, to energy, to defense. AUVs are power-constrained platforms operating in remote environments with fixed battery capacities, where propulsion competes with compute and sensors for power over lengthy mission durations. AUVs frequently operate in dark or turbid waters where optical sensing is of limited value, and rely on sonar as their primary sensing modality. Convolutional neural networks (CNNs) are the state-of-the-art solution for object detection in forward-looking sonar imagery, but are energy expensive (e.g. YOLOv8m: 322 mJ/inference). Spiking neural networks (SNNs) rely on binary spike activations and thus sparse accumulate-only operations, allowing them to be remarkably energy efficient, particularly when paired with dedicated neuromorphic hardware. The sparse, high-contrast structure of forward-looking sonar (FLS) returns is structurally matched to spike coding in a way that optical imagery is not. No prior work has assessed the suitability of SNNs for object detection in FLS imagery. SpikeYOLO, a fully spiking network trained with surrogate gradients, was benchmarked against state-of-the-art CNN baselines on three FLS object detection datasets. Key results: SpikeYOLO T=2 achieves 3.3$\times$ lower theoretical compute energy on UATD (97 vs 322 mJ) at competitive accuracy (0.529 mAP@0.5:0.95 vs. YOLOv8m's 0.575); SpikeYOLO matches YOLOv8m on mAP@0.5 and outperforms YOLO-SONAR and Fast R-CNN baselines on the sparse Marine-Debris-FLS dataset at 4.4$\times$ lower energy; SpikeYOLO demonstrates superior robustness to multiplicative speckle noise (3.0% degradation at $σ{=}0.4$ vs. 8.9% for YOLOv8m), outperforming YOLOv8m outright at $σ{=}0.6$, directly relevant to real-world FLS deployment.
Synthetic Aperture Sonar (SAS) is core for wide-area detection of small underwater targets. However, large-scale, high-quality SAS datasets are scarce, hindering data-driven recognition. Existing benchmarks are small and limited to single scenarios, failing to reproduce complex acoustic scattering, diverse seabeds, and multi-pose imaging in real detection. To fill this gap, we introduce SCTD 3.0 - a large-scale real-measured dataset for Sonar Common Target Detection in the Wild in natural waters. It contains over 10,000 high-quality real SAS image snippets from multi-frequency systems (240 kHz, 450 kHz, and others), covering ten typical target categories across varied seabed geomorphologies, with multiple observation angles, detection ranges, and frequency bands. We establish a rigorous hierarchical annotation protocol that decouples labeling of intrinsic physical properties, deployment characteristics, and scattering phenomena - covering material, geometry, internal structure, burial state, shadow integrity, specular highlights, edge diffraction, and resonance effects. This enables fine-grained target characterization. We also construct a multi-task benchmark for object detection, fine-grained classification, and attribute prediction, evaluating mainstream deep learning models under cross-domain, cross-scene, cross-frequency, and cross-view generalization. SCTD 3.0 is expected to provide a critical data cornerstone for robust underwater target perception in open-water environments. SCTD 3.0 is available at https://github.com/automlresearch/SCTD-3.0.