An Improved Deep Neural Network for Small-Ship Detection in SAR Imagery

With the proliferation of space-based optical systems and corresponding increased volume of overhead imagery data, there is a growing need for automatic target recognition (ATR) algorithms that both effectively identify objects of interest and remove the burden of analysis from a finite number of human ground operators. Although recent state-of-the-art (SOTA) deep learning architectures like Convolutional Neural Networks (CNNs) and Detection Transformers (DETRs) have shown great performance in accomplishing these tasks, this performance can degrade substantially when operating on data containing unexpected anomalies outside their original training sets. Likewise, the increased amount of automation in these processes magnifies the negative impact that an ATR algorithm can cause prior to a human analyst recognizing any problem in the data downstream. Spacebased optical systems rely on accurate calibration to create clean imagery, and as such, these ATR algorithms are subject to sensor calibration artifacts. Previous work has characterized common calibration artifacts such as sensor noise and failed detector artifacts as they affect the performance of an Inceptionv1-based ATR algorithm. This paper looks to extend this analysis to multiple CNN and transformer-based object detection architectures to characterize differences in performance degradation across various SOTA ATR algorithms. Notably, we found the RT-DETR architecture is more robust to uniformly distributed random scaling factors and random pixel failures than YOLOv8, YOLOv9, and Faster-RCNN particularly when detecting large objects like container ships and tankers. These results are useful in summarizing the expected performance impact of common calibration artifacts on ATR algorithms as well as informing algorithm selection when designing systems that leverage overhead imagery.

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Ship Detection in Synthetic Aperture Radar Images Based on BiLevel Spatial Attention and Deep Poly Kernel Network

Tian,

Jin,

Gao

et al. 2024

JMSE

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Synthetic aperture radar (SAR) is a technique widely used in the field of ship detection. However, due to the high ship density, fore-ground-background imbalance, and varying target sizes, achieving lightweight and high-precision multiscale ship object detection remains a significant challenge. In response to these challenges, this research presents YOLO-MSD, a multiscale SAR ship detection method. Firstly, we propose a Deep Poly Kernel Backbone Network (DPK-Net) that utilizes the Optimized Convolution (OC) Module to reduce data redundancy and the Poly Kernel (PK) Module to improve the feature extraction capability and scale adaptability. Secondly, we design a BiLevel Spatial Attention Module (BSAM), which consists of the BiLevel Routing Attention (BRA) and the Spatial Attention Module. The BRA is first utilized to capture global information. Then, the Spatial Attention Module is used to improve the network’s ability to localize the target and capture high-quality detailed information. Finally, we adopt a Powerful-IoU (P-IoU) loss function, which can adjust to the ship size adaptively, effectively guiding the anchor box to achieve faster and more accurate detection. Using HRSID and SSDD as experimental datasets, mAP of 90.2% and 98.8% are achieved, respectively, outperforming the baseline by 5.9% and 6.2% with a model size of 12.3 M. Furthermore, the network exhibits excellent performance across various ship scales.

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An Improved Deep Neural Network for Small-Ship Detection in SAR Imagery

Cited by 4 publications

References 42 publications

BFEA: A SAR Ship Detection Model Based on Attention Mechanism and Multiscale Feature Fusion

BFEA: A SAR Ship Detection Model Based on Attention Mechanism and Multiscale Feature Fusion

A comparative analysis of optical sensor artifacts across neural network-based ATR algorithms

Ship Detection in Synthetic Aperture Radar Images Based on BiLevel Spatial Attention and Deep Poly Kernel Network

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