Lightweight Bayesian SAR Image Object Detection and Recognition Method Based on Heavy-Tail Prior and Variational Inference

Traditional Bayesian SAR detection methods suffer poor adaptability to speckle noise, fail to handle severe class imbalance within large-scale multi-target datasets, and incur prohibitive training overheads. To address these drawbacks, this paper develops a lightweight Bayesian detection and recognition framework built upon heavy-tailed Laplacian priors and variational inference. We adopt ResNet-50 as the feature extraction backbone and design a four-stage pipeline: First, a noise-aware Laplacian heavy-tailed prior is proposed to strengthen resistance against speckle outliers. Second, a multi-class variational inference module is constructed to eliminate detection bias induced by uneven sample distribution across target categories. Third, a lightweight uncertainty feedback strategy is introduced to cut computational costs for large-batch training. Evaluated on the MSAR-1.0 dataset, our approach achieves an mAP@0.5 of 94.98% and a macro balanced accuracy (BA) of 93.34%. Compared with existing Bayesian detectors, the mAP metric rises by 5.44–6.53%. The model only consumes 4.33 ms per inference frame and completes full training within 1.53 h on a single GPU. Ablation tests validate the independent and combined efficacy of all three core modules. This integrated architecture balances detection precision, classification reliability, and training efficiency, offering a promising prototype for multi-class SAR target interpretation under the evaluated benchmark constraints.