The inability of conventional algorithms to detect infrared (IR) ship targets in complex scenes led to the development of detection methods based on convolutional neural networks (CNNs). In this study, we propose a CNN-based stacked residual coordinate attention network (SRCANet) for detecting IR ship targets. Three-directional stacked interaction modules and a full-scale skip connection feature fusion scheme are introduced. The proposed network maintains and integrates sufficient contextual information of IR ship targets and obtains clear target boundary information. A cascaded residual coordinate attention module (CRCAM) is designed as the basic node in the SRCANet. In addition, a residual coordinate attention module (RCAM) is introduced, which combines a 2-D convolution layer with batch normalization and rectified linear unit (CBR), a coordination attention module, and a residual connection. The RCAM enhances the input feature map and improves the representability of objects of interest. The CRCAM comprises several cascading RCAMs that deepen the feature extraction layers. Furthermore, because there is no publicly available IR ship target dataset for segmentation, pixel-level annotations are performed on a set of IR ship target images and released as a single-frame IR ship detection (SISD) dataset. Extensive experiments were conducted on the SISD dataset and the widely used single-frame IR small target (SIRST) dataset to demonstrate the superiority of the proposed method. The results indicate that the SRCANet outperforms the state-of-the-art models, and it is more robust when target texture information is lacking. The SISD dataset is available at https://github.com/echo-sky/SISD.

SRCANet: Stacked Residual Coordinate Attention Network for Infrared Ship Detection

The inability of conventional algorithms to detect infrared (IR) ship targets in complex scenes led to the development of detection methods based on convolutional neural networks (CNNs). In this study, we propose a CNN-based stacked residual coordinate attention network (SRCANet) for detecting IR ship targets. Three-directional stacked interaction modules and a full-scale skip connection feature fusion scheme are introduced. The proposed network maintains and integrates sufficient contextual information of IR ship targets and obtains clear target boundary information. A cascaded residual coordinate attention module (CRCAM) is designed as the basic node in the SRCANet. Additionally, a residual coordinate attention module (RCAM) is introduced, which combines a two-dimensional convolution layer with batch normalisation and rectified linear unit (CBR), a coordination attention module, and a residual connection. The RCAM enhances the input feature map and improves the representability of objects of interest. The CRCAM comprises several cascading RCAMs that deepen the feature extraction layers. Furthermore, because there is no publicly available IR ship target dataset for segmentation, pixel-level annotations are performed on a set of IR ship target images and released as a single-frame IR ship detection (SISD) dataset. Extensive experiments were conducted on the SISD dataset and the widely used single-frame IR small target dataset to demonstrate the superiority of the proposed method. The results indicate that the SRCANet outperforms the state-of-the-art models, and it is more robust when target texture information is lacking. The SISD dataset is available at https://github.com/echo-sky/SISD. 

Convolutional neural network (CNN)-based detection has shown great potential in accurate infrared (IR) ship detection. Typically, IR images exhibit a lack of texture details, whereas the sizes of IR ship targets are extremely multiscale, making it difficult to accurately detect IR ship targets. Herein, we propose a novel strengthened asymmetric receptive field block (SARFB) for accurate IR ship detection. The SARFB contains an asymmetric receptive field block (ARFB), a spatial pyramid pooling (SPP) block, and skip connections. Through these components, SARFB is able to fuse local and global features, enriching the expressive ability and receptive field of the network for multiscale IR ship target detection. Furthermore, because there is no publicly available IR ship target dataset for detection, we created the single-frame IR ship detection (SFISD) dataset, providing the first public benchmark for testing IR ship target detection performance. In comparative studies, the mAP_0.5 of Yolov5 with SARFB reached 93.3%, outperforming other state-of-the-art methods. Finally, we performed experiments on an unmanned surface vehicle (USV) equipped with an IR camera. The results show the superior robustness of our proposed method, especially when target texture information is lacking, and when the IR ship targets are multiscale. The SFISD dataset is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/echoo-sky/SFISD</uri> . 

SARFB: Strengthened Asymmetric Receptive Field Block for Accurate Infrared Ship Detection

Infrared images exhibit considerable variations in probability distributions, stemming from the utilization of distinct infrared sensors and the influence of diverse environmental conditions. The variations pose great challenges for deep learning models to detect ship objects and adapt to unseen maritime environments. To address the domain shift problem, we propose an end-to-end infrared ship object detection model based on meta-learning neural network to improve domain adaptation for target domain where data is not available at training phase. Different from existing domain generalization methods, the novelty of our model lies in the effective exploition of meta-learning and domain adaptation, ensuring that the extracted domain-independent features are meaningful and domain-invariant at the semantic level. Firstly, a double gradient-based meta-learning algorithm is designed to solve the common optimal descent direction between different domains through two gradient updates in the inner and outer loops. The algorithm enables extraction of domain-invariant features from the pseudo-source and pseudo-target domain data. Secondly, a domain discriminator with dynamic-weighted gradient reversal layer (DWGRL) is designed to accurately classify domain-invariant features and provide additional global supervision information. Finally, a multi-scale feature aggregation method is proposed to improve the extraction of multi-scale domain-invariant features. It can effectively fuse local features at different scales and global features of targets. Extensive experimental results conducted in real nighttime water surface scenes demonstrate that the proposed model achieves very high detection accuracy on target domain data, even no target domain data was used during the training phase. Compared to the existing methods, our method not only improves the detection accuracy of infrared ships by 18%, but also exhibits the smallest standard deviation with a value of 0.93, indicating its superior generalization performance. 

Meta-learning based infrared ship object detection model for generalization to unknown domains

Abstract Bilateral filtering is an image processing technique commonly adopted as intermediate step of several computer vision tasks. Opposite to the conventional image filtering, which is based on convolving the input pixels with a static kernel, the bilateral filtering computes its weights on the fly according to the current pixel values and some tuning parameters. Such additional elaborations involve nonlinear weighted averaging operations, which make difficult the deployment of bilateral filtering within existing vision technologies based on real-time and low-energy hardware architectures. This paper presents a new approximation strategy that aims to improve the energy efficiency of circuits implementing the bilateral filtering function, while preserving their real-time performances and elaboration accuracy. In contrast to the state-of-the-art, the proposed technique allows the filtering action to be on the fly adapted to both the current pixel values and to the tuning parameters, thus avoiding any architectural modification or tables update. When hardware implemented within the Xilinx Zynq XC7Z020 FPGA device, a 5 × 5 filter based on the proposed method processes 237.6 Mega pixels per second and consumes just 0.92 nJ per pixel, thus improving the energy efficiency by up to 2.8 times over the competitors. The impact of the proposed approximation on three different imaging applications has been also evaluated. Experiments demonstrate reasonable accuracy penalties over the accurate counterparts. 

Approximate bilateral filters for real-time and low-energy imaging applications on FPGAs

High anti-interference and FPGA-oriented method for real-time ship detection based on structured LBP features

With the upgrade of navigation signal system system, the innovation of Beidou system has already entered a stage of fast developing, Beidou-3system. The quality of satellite signals directly impacts the performance of satellite navigation system, as well as the performance of receivers, such as airborne, shipborne and on-board GNSS receivers. The research on signal quality evaluation method has important theoretical and application significance to improve the performance of satellite navigation receivers. This paper introduces the Beidou-3 signal quality evaluation parameters and measurement methods, and discusses the power spectral density, eye diagram, constellation diagram, correlation loss and S-curve deviation of the Beidou-3 navigation signal.

Analysis and research on the signal quality of Beidou-3 system

The sophistication of ship detection technology in remote sensing images is insufficient, the detection results differ substantially from the practical requirements, mainly reflected in the inadequate support for the differentiated application of multi-scene, multi-resolution and multi-type target ships. To overcome these challenges, a ship detection method based on multiscale feature extraction and lightweight CNN is proposed. Firstly, the candidate-region extraction method, based on a multiscale model, can cover the potential targets under different backgrounds accurately. Secondly, the multiple feature fusion method is employed to achieve ship classification, in which, Fourier global spectrum features are applied to discriminate between targets and simple interference, and the targets in complex interference scenarios are further distinguished by using lightweight CNN. Thirdly, the cascade classifier training algorithm and an improved non-maximum suppression method are used to minimise the classification error rate and maximise generalisation, which can achieve final-target confirmation. Experimental results validate our method, showing that it significantly outperforms the available alternatives, reducing the model size by up to 2.17 times while improving detection performance be improved by up to 5.5% in multi-interference scenarios. Furthermore, the robustness ability was verified by three indicators, among which the F-measure score and true–false-positive rate can increase by up to 5.8% and 4.7% respectively, while the mean error rate can decrease by up to 38.2%.

/pdf/robust-ship-detection-in-infrared-images-through-multiscale-22x9gtko.pdf

Robust Ship Detection in Infrared Images through Multiscale Feature Extraction and Lightweight CNN

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High anti-interference and FPGA-oriented method for real-time ship detection based on structured LBP features

Analysis and research on the signal quality of Beidou-3 system

Robust Ship Detection in Infrared Images through Multiscale Feature Extraction and Lightweight CNN