An Infrared Maritime Small Target Detection Algorithm Based on Semantic, Detail, and Edge Multidimensional Information Fusion

Yao, Jiping; Xiao, Shanzhu; Deng, Qiuqun; Wen, Gongjian; Tao, Huamin; Du, Jinming

doi:10.3390/rs15204909

Cited by 3 publications

(2 citation statements)

References 47 publications

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“…Some visualization examples of the MFIRST, SIRST, and IRSTD-1k datasets are shown in [12][13][14][15][16][17][18][19], respectively. The yellow circles in the images indicate false alarms, and the red circle indicates leakage detection.…”

Section: Visual Comparisonmentioning

confidence: 99%

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Multiscale Feature Extraction U-Net for Infrared Dim- and Small-Target Detection

Wang,

Han,

et al. 2024

Remote Sensing

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The technology of infrared dim- and small-target detection is irreplaceable in many fields, such as those of missile early warning systems and forest fire prevention, among others. However, numerous components interfere with infrared imaging, presenting challenges for achieving successful detection of infrared dim and small targets with a low rate of false alarms. Hence, we propose a new infrared dim- and small-target detection network, Multiscale Feature Extraction U-Net for Infrared Dim- and Small-Target Detection (MFEU-Net), which can accurately detect targets in complex backgrounds. It uses the U-Net structure, and the encoders and decoders consist of ReSidual U-block and Inception, allowing rich multiscale feature information to be extracted. Thus, the effectiveness of algorithms in detecting very small-sized targets can be improved. In addition, through the multidimensional channel and spatial attention mechanism, the model can be adjusted to focus more on the target area in the image, improving its extraction of target information and detection performance in different scenarios. The experimental results show that our proposed algorithm outperforms other advanced algorithms in detection performance. On the MFIRST, SIRST, and IRSTD-1k datasets, we achieved detection rates of 0.864, 0.962, and 0.965; IoU values of 0.514, 0.671, and 0.630; and false alarm rates of 3.08 × 10−5, 2.61 × 10−6, and 1.81 × 10−5, respectively.

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Section: Visual Comparisonmentioning

confidence: 99%

“…Due to the sparse nature of IDSTs, some algorithms enhance the visibility of targets by suppressing the background [12]. There are also algorithms that use GAN networks to separately address the problem of missed and false alarms, using different generators to address the difficult balance between them [13].…”

Section: Introductionmentioning

confidence: 99%

Multiscale Feature Extraction U-Net for Infrared Dim- and Small-Target Detection

Wang,

Han,

et al. 2024

Remote Sensing

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Maritime Electro-Optical Image Object Matching Based on Improved YOLOv9

Yang,

Cao,

Liu

et al. 2024

Electronics

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The offshore environment is complex during automatic target annotation at sea, and the difference between the focal lengths of visible and infrared sensors is large, thereby causing difficulties in matching multitarget electro-optical images at sea. This study proposes a target-matching method for visible and infrared images at sea based on decision-level topological relations. First, YOLOv9 is used to detect targets. To obtain markedly accurate target positions to establish accurate topological relations, the YOLOv9 model is improved for its poor accuracy for small targets, high computational complexity, and difficulty in deployment. To improve the detection accuracy of small targets, an additional small target detection head is added to detect shallow feature maps. From the perspective of reducing network size and achieving lightweight deployment, the Conv module in the model is replaced with DWConv, and the RepNCSPELAN4 module in the backbone network is replaced with the C3Ghost module. The replacements significantly reduce the number of parameters and computation volume of the model while retaining the feature extraction capability of the backbone network. Experimental results of the photovoltaic dataset show that the proposed method improves detection accuracy by 8%, while the computation and number of parameters of the model are reduced by 5.7% and 44.1%, respectively. Lastly, topological relationships are established for the target results, and targets in visible and infrared images are matched based on topological similarity.

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Infrared Bilateral Polarity Ship Detection in Complex Maritime Scenarios

Lu,

Teng,

Tan

et al. 2024

Sensors

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In complex maritime scenarios where the grayscale polarity of ships is unknown, existing infrared ship detection methods may struggle to accurately detect ships among significant interference. To address this issue, this paper first proposes an infrared image smoothing method composed of Grayscale Morphological Reconstruction (GMR) and a Relative Total Variation (RTV). Additionally, a detection method considering the grayscale uniformity of ships and integrating shape and spatiotemporal features is established for detecting bright and dark ships in complex maritime scenarios. Initially, the input infrared images undergo opening (closing)-based GMR to preserve dark (bright) blobs with the opposite suppressed, followed by smoothing the image with the relative total variation model to reduce clutter and enhance the contrast of the ship. Subsequently, Maximally Stable Extremal Regions (MSER) are extracted from the smoothed image as candidate targets, and the results from the bright and dark channels are merged. Shape features are then utilized to eliminate clutter interference, yielding single-frame detection results. Finally, leveraging the stability of ships and the fluctuation of clutter, true targets are preserved through a multi-frame matching strategy. Experimental results demonstrate that the proposed method outperforms ITDBE, MRMF, and TFMSER in seven image sequences, achieving accurate and effective detection of both bright and dark polarity ship targets.

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An Infrared Maritime Small Target Detection Algorithm Based on Semantic, Detail, and Edge Multidimensional Information Fusion

Cited by 3 publications

References 47 publications

Multiscale Feature Extraction U-Net for Infrared Dim- and Small-Target Detection

Multiscale Feature Extraction U-Net for Infrared Dim- and Small-Target Detection

Maritime Electro-Optical Image Object Matching Based on Improved YOLOv9

Infrared Bilateral Polarity Ship Detection in Complex Maritime Scenarios

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