CNN‐based infrared dim small target detection algorithm using target‐oriented shallow‐deep features and effective small anchor

Du, Jinming; Lü, Huanzhang; Hu, Moufa; Zhang, Luping; Shen, Xinglin

doi:10.1049/ipr2.12001

Cited by 72 publications

(58 citation statements)

References 43 publications

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“…According to the analysis of MTS-UAV data set, the data set contains a large number of small targets, and the size is less than 8 when using 8-fold down sampling feature map for detection × The target of 8 occupies less than one pixel in the feature map, and its position information is lost in the feature map. Du et al [11] proved the key role of shallow features in small target detection. On this basis, we use the 4x down sampling features of the feature extraction network to replace the 8x down sampling features as the output, that is, the 4x, 16x and 32x down sampling features are input into the feature fusion network.…”

Section: Improve Network Structurementioning

confidence: 99%

Infrared target detection method using adaptive feature fusion

Tongtong

Peng

et al. 2022

J. Phys.: Conf. Ser.

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As the key technology of low altitude airspace security and air attack, vision-based air target detection has better concealment and lower cost compared with radar, radio and other detection methods. Based on yolov3, this paper proposes an air infrared target detection algorithm using adaptive feature fusion, which can effectively improve the accuracy of air infrared multi-scale target detection. Firstly, the construction of air infrared target detection data set is completed, and the target characteristics in the data set are statistically analysed. Secondly, the output layer of backbone is adjusted, and the four times down sampling feature map is used to replace the eight times down sampling feature map to enhance the detection ability of small targets. Thirdly, aiming at the problem of weakening feature expression caused by cross-scale feature fusion in FPN, a pixel level feature adaptive fusion module is designed. Finally, two improved SPP modules are added to neck to enrich the expression ability of feature map through the fusion of local features and global features. Experimental results show that compared with yolov3, our algorithm improves the map from 86.53% to 90.26% while keeping the detection speed basically unchanged.

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Section: Improve Network Structurementioning

confidence: 99%

Infrared target detection method using adaptive feature fusion

Tongtong

Peng

et al. 2022

J. Phys.: Conf. Ser.

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show abstract

“…Zhao et al proposed a generative adversarial network (GAN)-based detection model [32] to detect the basic characteristics of infrared small targets. In addition, a series of deep learning networks [15,16,33,34] have been designed to recognise the basic features of infrared small targets, with the aim of improving the detection accuracy. The attention mechanism has been demonstrated to enhance the contextual information of features by focusing the network on key areas [35].…”

Section: Infrared Small Target Detectionmentioning

confidence: 99%

“…The proposed architecture mainly consists of two components-U-Net [31] as a host network, with the proposed EAA module performing the cross-layer feature fusion operation; ResNet-20 [48] as the backbone architecture-as shown in Table 1. The deeper layers of the network are designed to be able to extract richer semantic information, as spatially finer shallow features and semantically stronger deeper features are considered crucial for detecting infrared small targets [34]. U-Net has natural advantages for infrared small target detection.…”

Section: Network Architecturementioning

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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“…Recently, the generalization ability of the data-driven infrared small-dim target detection is well promoted by deep learning methods [24][25][26][27][28]. [4] used adversarial generation networks (GANs) to balance Miss Detection (MD) and False Alarm (FA).…”

Section: Related Workmentioning

confidence: 99%

Infrared Small-Dim Target Detection with Transformer under Complex Backgrounds

Liu¹,

Gao²,

Chen³

et al. 2021

Preprint

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The infrared small-dim target detection is one of the key techniques in the infrared search and tracking system. Since the local regions similar to infrared small-dim targets spread over the whole background, exploring the interaction information amongst image features in large-range dependencies to mine the difference between the target and background is crucial for robust detection. However, existing deep learningbased methods are limited by the locality of convolutional neural networks, which impairs the ability to capture large-range dependencies. To this end, we propose a new infrared small-dim target detection method with the transformer. We adopt the selfattention mechanism of the transformer to learn the interaction information of image features in a larger range. Additionally, we design a feature enhancement module to learn more features of small-dim targets. After that, we adopt a decoder with the U-Net-like skip connection operation to get the detection result. Extensive experiments on two public datasets show the obvious superiority of the proposed method over state-of-the-art methods.

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CNN‐based infrared dim small target detection algorithm using target‐oriented shallow‐deep features and effective small anchor

Cited by 72 publications

References 43 publications

Infrared target detection method using adaptive feature fusion

Infrared target detection method using adaptive feature fusion

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

Infrared Small-Dim Target Detection with Transformer under Complex Backgrounds

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