Magnetic Target Image Recognition via an Enhanced YOLOv8 Framework

Detecting deeply buried weak-signature ferromagnetic targets is essential for mine clearance and anti-submarine missions, yet conventional detection algorithms struggle to balance recognition accuracy and computational overhead under strong geomagnetic clutter. This work develops a lightweight detection framework tailored to low-SNR magnetic contour maps, named YOLOv8n-M3CA. The framework adopts MobileNetV3 as the lightweight backbone and introduces Coordinate Attention to strengthen the extraction of faint spatial magnetic anomaly features. We build a COMSOL (Multiphysics 6.2) simulation dataset with 1076 magnetic images covering five target geometries. The dataset is divided into 861 training samples and 215 test samples with 1519 annotated targets overall. Comparative experiments demonstrate that our model reduces parameters from 3.16 M to 2.78 M and GFLOPs from 8.9 to 6.5, while boosting precision, recall, and F1-score to 0.996, 0.994, and 0.995; its training time is shortened to 11.01 h, achieving an inference FPS of 21.5 on CPU. In a preliminary field test on a buried rectangular target, our method attains a detection confidence of 0.73, far exceeding the baseline YOLOv8n at 0.34. Experimental results verify that the proposed framework delivers notable anti-interference performance toward faint magnetic signals, though further field validation with diverse targets and conditions remains necessary before full operational deployment.