Hongjie He;Tianwen Hu;Sheng Xu;Hong Xu;Lin Song;Zhongpei Sun
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引用次数: 0
Abstract
To address the critical challenges in synthetic aperture radar (SAR) ship target detection, including complex background speckle noise interference and the difficulty in balancing model lightweight design with detection accuracy, this article proposes an innovative PPDM-YOLO model. Through modular architecture design, we establish a four-part technical framework: First, a lightweight feature extraction module named PCA is developed to reduce computational complexity by analyzing feature map redundancy, effectively mitigating feature degradation caused by noise. Second, the noise-resistant enhancement module, PSA-G, integrates the multiscale adaptive gradient threshold module with a dynamic spatial attention mechanism. This integration enhances target feature representation while effectively suppressing noise interference. Third, DySample technology is employed in place of conventional upsampling methods to improve the quality of feature reconstruction and preserve spatial details. In addition, a multiscale fusion small target detection network is introduced to boost small object detection through cross-layer feature interaction. Experimental results on HRSID and SSDD datasets demonstrate that PPDM-YOLO achieves 93.7% mAP50 and 70.3% mAP50–95 on HRSID, while reaching 99.4% mAP50 and 78.7% mAP50–95 on SSDD, showing significant advantages over mainstream detection models. With 34.7% fewer parameters than YOLOv11n, our model achieves optimal balance among noise suppression, model lightweighting, and detection accuracy. This research provides an efficient and reliable technical solution for real-time SAR ship detection in complex marine environments.
期刊介绍:
The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.