Scale Expansion Pyramid Network for Cross-Scale Object Detection in Sar Images

Zhou, Zheng; Guan, R.; Cui, Zongyong; Cao, Zongjie; Pi, Yiming; Yang, Jianyu

doi:10.1109/igarss47720.2021.9554986

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…The input mode can be counted as a multi-scale training test [81]; it is yet endowed with a new idea, i.e., establishing multi-scale inputs in a single scale. It can solve cross-scale detection (targets have a large pixel scale difference [76]). The large scale difference is often due to the large resolution difference [82,83].…”

Section: Input Modementioning

confidence: 99%

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Scale in Scale for SAR Ship Instance Segmentation

et al. 2023

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Ship instance segmentation in synthetic aperture radar (SAR) images can provide more detailed location information and shape information, which is of great significance for port ship scheduling and traffic management. However, there is little research work on SAR ship instance segmentation, and the general accuracy is low because the characteristics of target SAR ship task, such as multi-scale, ship aspect ratio, and noise interference, are not considered. In order to solve these problems, we propose an idea of scale in scale (SIS) for SAR ship instance segmentation. Its essence is to establish multi-scale modes in a single scale. In consideration of the characteristic of the targeted SAR ship instance segmentation task, SIS is equipped with four tentative modes in this paper, i.e., an input mode, a backbone mode, an RPN mode (region proposal network), and an ROI mode (region of interest). The input mode establishes multi-scale inputs in a single scale. The backbone mode enhances the ability to extract multi-scale features. The RPN mode makes bounding boxes better accord with ship aspect ratios. The ROI mode expands the receptive field. Combined with them, a SIS network (SISNet) is reported, dedicated to high-quality SAR ship instance segmentation on the basis of the prevailing Mask R-CNN framework. For Mask R-CNN, we also redesign (1) its feature pyramid network (FPN) for better small ship detection and (2) its detection head (DH) for a more refined box regression. We conduct extensive experiments to verify the effectiveness of SISNet on the open SSDD and HRSID datasets. The experimental results reveal that SISNet surpasses the other nine competitive models. Specifically, the segmentation average precision (AP) index is superior to the suboptimal model by 4.4% on SSDD and 2.5% on HRSID.

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Section: Input Modementioning

confidence: 99%

“…The input mode establishes an image pyramid at the network input end to handle crossscale ship detection (large size differences [76]). The backbone mode establishes multiple hierarchical residual-like connections in a single layer to extract multi-scale features with increased receptive fields at the granular level [77].…”

Section: Introductionmentioning

confidence: 99%

Scale in Scale for SAR Ship Instance Segmentation

et al. 2023

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“…However, surveillance images (such as photographs or videos) for ships are rare, while Synthetic Aperture Radar (SAR) is available all day under all weather. Thus, for ship object detection, methods for detection with SAR images are proposed, such as DDNet [8], Saliency-Based Centernet [8], and Expansion Pyramid Network (SEPN) [9]. For general ship surveillance images, K-means clustering prior box combined with the yolov4 network [10] and SSD_MobilenetV2 [11] have been applied to improve ship detection performance.…”

Section: Object Detectionmentioning

confidence: 99%

“…Thus, the corresponding larger feature map can be obtained to detect smaller targets. We add a smaller anchor (5,6,7,9,12,10), thus adding a feature map of 160 × 160 to improve the detection ability of buoys in object detection.…”

Section: Small Object Detection Anchormentioning

confidence: 99%

Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning

Wen,

Gucma,

et al. 2023

Applied Sciences

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With the rapid development of artificial intelligence technology and unmanned surface vehicle (USV) technology, object detection and tracking have wide applications in marine monitoring and intelligent ships. However, object detection and tracking tasks on small sample datasets often face challenges due to insufficient sample data. In this paper, we propose a ship detection and tracking model with high accuracy based on a few training samples with supervised information based on the few-shot learning framework. The transfer learning strategy is designed, innovatively using an open dataset of vehicles on highways to improve object detection accuracy for inland ships. The Shuffle Attention mechanism and smaller anchor boxes are introduced in the object detection network to improve the detection accuracy of different targets in different scenes. Compared with existing methods, the proposed method is characterized by fast training speed and high accuracy with small datasets, achieving 84.9% (mAP@0.5) with only 585 training images.

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