SLA-YOLO: A Lightweight SAR Ship Detection Model

Abstract

Reliable ship detection in synthetic aperture radar (SAR) imagery is challenged by speckles, coastal clutter, scale variation, dense berthing, weak target responses, and limited computing resources. SLA-YOLO is a lightweight YOLO11n detector that coordinates established mechanisms across the backbone, neck, and head. LESCNet combines ECA-enhanced depthwise-pointwise convolution with residual large-kernel refinement; C3k2-DAPE applies input-conditioned branch weighting and channel recalibration at selected fusion blocks; and SA-LSCD shares intermediate transformations while retaining scale-specific predictors. On fixed SSDD and HRSID splits, SLA-YOLO achieves mAP@0.5 values of 97.3% and 90.8% and mAP@0.5:0.95 values of 68.2% and 64.3%, respectively, with 1.70 million parameters and 3.6 GFLOPs. Relative to YOLO11n, parameters and FLOPs decrease by 34.1% and 42.9%, while mAP@0.5 increases by 4.4 and 5.2 percentage points, respectively. A complete 23 ablation shows positive average main effects for all three components and context-dependent interactions. Across four no-fine-tuning transfer settings, SLA-YOLO improves mAP@0.5 by 3.5–6.3 points. Three-seed experiments yield 97.17 ± 0.31% and 90.87 ± 0.35% mAP@0.5 on SSDD and HRSID. Small-ship AP improves over YOLO11n by 10.1 ± 0.5 and 6.9 ± 0.7 points, with larger gains inshore. Network-only throughput reaches 66.8 and 63.4 FPS on an RTX 3090; embedded-device performance remains unverified.