A Systematic Benchmark of Object Detectors for Small-Sample Side-Scan Sonar Mine Detection

The detection of sea mines in side-scan sonar imagery is hindered by small object size, heavy speckle noise, and severe data scarcity. Existing studies mostly transplant improvements designed for natural images to sonar scenarios, and their effectiveness under small-sample conditions lacks unified verification. This study presents a systematic benchmark of lightweight object detectors on a publicly available side-scan sonar mine-like object dataset, SSS-MLOD. Under a unified protocol, five YOLO variants are compared under from-scratch training and COCO-pretrained transfer learning, and 25 structural, loss-based, and data-side modifications are evaluated. The results show that differences among lightweight baselines are marginal (mAP50 of 0.732 to 0.753), transfer learning on only 243 training images yields the largest single gain (mAP50 from 0.742 to 0.856 for YOLOv13n), and none of the 25 modifications was observed to improve the baseline under from-scratch training on this dataset. Increasing the input resolution improves mAP50:95 by 0.044 to 0.049 for YOLOv5n and YOLOv8n but degrades the performance of YOLOv11n, indicating that the benefit of resolution is architecture-dependent; test-time augmentation with a tuned confidence threshold raises recall by 0.035 under a precision constraint of 0.85. The best configuration, YOLOv13n with transfer learning, achieves an mAP50 of 0.856 and a recall of 0.757. These findings provide an evidence-based reference for detector selection and configuration in resource constrained applications such as autonomous underwater vehicles.