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HE Shicun, WANG Xinchao, and CHEN Siwei. SAR image artificial target detection based on YOLO model merging[J]. Journal of Radars, in press. doi: 10.12000/JR26080
Citation: HE Shicun, WANG Xinchao, and CHEN Siwei. SAR image artificial target detection based on YOLO model merging[J]. Journal of Radars, in press. doi: 10.12000/JR26080
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SAR Image Artificial Target Detection Based on YOLO Model Merging

DOI: 10.12000/JR26080 CSTR: 32380.14.JR26080

  • College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Funds:  The National Natural Science Foundation of China (U24B20189, 62122091), The Science and Technology Innovation Program of Hunan Province (2024RC1040)
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  • Corresponding author: CHEN Siwei, chenswnudt@163.com
  • Received Date: 2026-04-29
  • Rev Recd Date: 2026-07-05
    • Available Online: 2026-07-08
    Abstract
  • High-value artificial target detection is a critical application of Synthetic Aperture Radar (SAR). Existing deep learning-based detection methods often fail to adequately reuse parameterized knowledge from trained models and demonstrate limited generalization across different datasets. This limitation hinders their practical application in new scenarios. To address this issue, this study proposes a SAR image artificial target detection method based on YOLO model merging, adopting a Learning From Models (LFM) approach. The core idea is to use multiple homogeneous models as knowledge sources. The method integrates the feature extraction, multiscale feature fusion, and target discrimination capabilities developed in these source models into a unified detection framework, thereby transforming existing model knowledge into improved detection performance. Specifically, based on a shared pretrained backbone, the neck network parameters of multiple source models are first transferred to the merged model. Multiscale feature fusion is then achieved through channel concatenation and point-wise convolution. In addition, parallel detection head branches are introduced at each feature scale to preserve the discriminative capabilities of the source models. Experimental results on three public SAR datasets (SADD, SSDD, and HRSID) demonstrate that the proposed method effectively improves the detection performance of man-made targets in SAR images. Consistent improvements are observed in Recall, mAP50, and mAP50-95, particularly under high intersection-over-union thresholds and limited-sample scenarios, where the proposed method exhibits superior robustness and generalization ability.

     

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