Channel-boosted multi-scale R-CNN for accurate and real-time ship and land detection in complex SAR scenes

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dc.contributor.authorHanbay, Kazim
dc.contributor.authorOzcelik, Salih Taha Alperen
dc.contributor.authorAltin, Mustafa
dc.contributor.authorUzen, Huseyin
dc.date.accessioned2026-04-04T13:34:55Z
dc.date.available2026-04-04T13:34:55Z
dc.date.issued2026
dc.departmentİnönü Üniversitesi
dc.description.abstractAccurate ship detection in Synthetic Aperture Radar (SAR) imagery is crucial for maritime surveillance but remains challenging due to small target sizes, speckle noise, and complex sea-surface backgrounds. While most existing methods focus exclusively on identifying ships, our approach also achieves reliable detection of land areas, providing an additional contribution to the literature. This study introduces CBM-RCNN (Channel-Boosted Multi-Scale R-CNN), a novel deep learning architecture that integrates Convolutional Block Attention Modules (CBAM) and a Bidirectional Feature Pyramid Network (BiFPN) on a ResNet50 backbone. CBAM enhances both spatial and channel-level feature representation, enabling reliable detection of small vessels, while BiFPN fuses multi-scale features bidirectionally, improving accuracy across vessels of different sizes and positions. CBM-RCNN was evaluated against standard Faster R-CNN and YOLOv8 models across diverse maritime scenes, including simple, densely populated, and visually complex scenarios. The model demonstrated superior detection accuracy, balanced class-specific performance, and strong generalization. It effectively resolves overlapping vessels, distinguishes ships from coastal structures, and maintains robustness under challenging SAR-specific noise conditions. Importantly, it achieves inference speeds suitable for near-real-time applications, highlighting practical applicability. By combining attention-driven refinement with multi-scale feature aggregation, CBM-RCNN addresses limitations of prior methods, particularly in small object recognition, complex scene generalization, and simultaneous land detection. This architecture provides a robust framework for automated maritime monitoring and offers a foundation for future improvements in large-scale SAR-based ship detection and environmental surveillance.
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [123E344]
dc.description.sponsorshipThis research was financially supported by the Scientific and Technological Research Council of Turkey (TUBITAK), (Project No: 123E344).
dc.identifier.doi10.1016/j.measurement.2025.120264
dc.identifier.issn0263-2241
dc.identifier.issn1873-412X
dc.identifier.scopus2-s2.0-105027326130
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://doi.org/10.1016/j.measurement.2025.120264
dc.identifier.urihttps://hdl.handle.net/11616/109497
dc.identifier.volume264
dc.identifier.wosWOS:001658627500003
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofMeasurement
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250329
dc.subjectShip detection
dc.subjectConvolutional block attention modules
dc.subjectBidirectional feature pyramid network
dc.subjectMaritime surveillance
dc.titleChannel-boosted multi-scale R-CNN for accurate and real-time ship and land detection in complex SAR scenes
dc.typeArticle

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