Image Small Target Detection based on Deep Learning with SNR Controlled Sample Generation

Liu, Ming; Du, Haoyuan; Zhao, Yuejin; Dong, Liquan; Hui, Mei

doi:10.1515/9783110584974-025

Cited by 39 publications

(28 citation statements)

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“…We simulate a synthetic image dataset, namely "Dataset1", by overlaying two-dimensional Gaussian function on a background image following the method in [37] and [38]. The Dataset1 contains 400 images of 2500 × 2500, in which the background image is collected from the thermal infrared sensor of landsat8 [40], including a variety of scenes, such as land, ocean, and clouds.…”

Section: A Datasetsmentioning

confidence: 99%

“…more important for practical applications. The single-frame based algorithms can be roughly divided into four categories: background feature-based algorithms [4][5][6][7][8][9], target featurebased algorithms [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26], image data structure feature-based algorithms [27][28][29][30][31][32][33], and deep learning-based algorithms [34][35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

“…The deep learning-based algorithms are easy to be influenced by complex background regions [36]. Liu et al [37] and Wang et al [38] designed different network structures and loss functions to improve detection accuracy and reduce false alarms and missing alarms. Due to the small amount of training data and the uneven distribution between the target and the background, the properties of the small targets cannot be fully learned in the deep learning model training.…”

Section: Introductionmentioning

confidence: 99%

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Small Target Detection From Infrared Remote Sensing Images Using Local Adaptive Thresholding

Liu

Xie

Dong

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Small target detection from infrared remote sensing image is a challenge task. In this paper, a novel local adaptive threshold algorithm combined with heterogeneity and compactness filters is proposed to detect the small target from the infrared remote sensing images. Firstly, the infrared image is filtered by a heterogeneity filter to enhance the target saliency. Then, the enhanced image is filtered by a compactness filter to generate target candidate region map. Finally, for each pixel in the target candidate region, a local adaptive threshold is calculated from the enhanced image to determine whether it is a target pixel or not, and thus the targets are extracted out. The designed heterogeneity filter and compactness filter can effectively suppress the background clutter, enhance the target, and generate target candidate regions. The proposed adaptive thresholding is a local threshold method, which is calculated in a small local window and can effectively reduce the false alarm and missing alarm. Qualitative and quantitative experiments are conducted on synthetic images and real images. The experiment results show that, with good target enhancement and background suppression, and high detection accuracy, the proposed method outperforms other state-of-the-art methods.Index Terms-infrared dim small target (IDST), remote sensing image, adaptive threshold, background suppression, target enhancement.

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Section: A Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Small Target Detection From Infrared Remote Sensing Images Using Local Adaptive Thresholding

Liu

Xie

Dong

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Over the last few decades, research has been conducted in various directions mentioned above and more studies are being conducted based on deep learning. Liu et al [33] proposed that training a sample using a signal-to-noise ratio (SNR) with an appropriate constant value helps improve the performance over training with a randomly sampled SNR. The targets were generated and synthesized randomly and were not actual targets.…”

Section: Related Workmentioning

confidence: 99%

Heterogeneous Gray-Temperature Fusion-Based Deep Learning Architecture for Far Infrared Small Target Detection

Ryu

Kim

2019

Journal of Sensors

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This paper proposes the end-to-end detection of a deep network for far infrared small target detection. The problem of detecting small targets has been a subject of research for decades and has been applied mainly in the field of surveillance. Traditional methods focus on filter design for each environment, and several steps are needed to obtain the final detection result. Most of them work well in a given environment but are vulnerable to severe clutter or environmental changes. This paper proposes a novel deep learning-based far infrared small target detection method and a heterogeneous data fusion method to solve the lack of semantic information due to the small target size. Heterogeneous data consists of radiometric temperature data (14-bit) and gray scale data (8-bit), which includes the physical meaning of the target, and compares the effects of the normalization method to fuse heterogeneous data. Experiments were conducted using an infrared small target dataset built directly on the cloud backgrounds. The experimental results showed that there is a significant difference in performance according to the various fusion methods and normalization methods, and the proposed detector showed approximately 20% improvement in average precision (AP) compared to the baseline constant false alarm rate (CFAR) detector.

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“…In contrast to traditional methods, CNN-based methods learn the features of infrared small targets through the training of neural networks with large amounts of data. Liu et al [14] were the first to propose the use of CNNs for infrared small target detection. Gao et al [15] subsequently proposed a high-precision dim and small target detection algorithm based on feature mapping with a spindle network structure.…”

Section: Introductionmentioning

confidence: 99%

EAAU-Net: Enhanced Asymmetric Attention U-Net for Infrared Small Target Detection

et al. 2021

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Detecting infrared small targets lacking texture and shape information in cluttered environments is extremely challenging. With the development of deep learning, convolutional neural network (CNN)-based methods have achieved promising results in generic object detection. However, existing CNN-based methods with pooling layers may lose the targets in the deep layers and, thus, cannot be directly applied for infrared small target detection. To overcome this problem, we propose an enhanced asymmetric attention (EAA) U-Net. Specifically, we present an efficient and powerful EAA module that uses both same-layer feature information exchange and cross-layer feature fusion to improve feature representation. In the proposed approach, spatial and channel information exchanges occur between the same layers to reinforce the primitive features of small targets, and a bottom-up global attention module focuses on cross-layer feature fusion to enable the dynamic weighted modulation of high-level features under the guidance of low-level features. The results of detailed ablation studies empirically validate the effectiveness of each component in the network architecture. Compared to state-of-the-art methods, the proposed method achieved superior performance, with an intersection-over-union (IoU) of 0.771, normalised IoU (nIoU) of 0.746, and F-area of 0.681 on the publicly available SIRST dataset.

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Image Small Target Detection based on Deep Learning with SNR Controlled Sample Generation

Cited by 39 publications

References 0 publications

Small Target Detection From Infrared Remote Sensing Images Using Local Adaptive Thresholding

Small Target Detection From Infrared Remote Sensing Images Using Local Adaptive Thresholding

Heterogeneous Gray-Temperature Fusion-Based Deep Learning Architecture for Far Infrared Small Target Detection

EAAU-Net: Enhanced Asymmetric Attention U-Net for Infrared Small Target Detection

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