Asymmetric Contextual Modulation for Infrared Small Target Detection

Dai, Yawen; Wu, Yiquan; Zhou, Fei; Barnard, Kobus

doi:10.1109/wacv48630.2021.00099

Cited by 244 publications

(205 citation statements)

References 31 publications

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“…Recently, deep learning based methods have attracted much attention due to its powerful feature learning ability. It is widely used in infrared small target detection [32]- [35]. Although they achieve improved performance, the main challenge of deep learning is that infrared small target lacks remarkable texture and shape features, which makes feature learning difficult.…”

Section: Dl-based Infrared Small Target Detection Methodsmentioning

confidence: 99%

Non-Convex Tensor Low-Rank Approximation for Infrared Small Target Detection

Liu,

Yang,

et al. 2021

Preprint

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Infrared small target detection plays an important role in many infrared systems. Recently, many infrared small target detection methods have been proposed, in which the lowrank model has been used as a powerful tool. However, most low-rank-based methods assign the same weights for different singular values, which will lead to inaccurate background estimation. Considering that different singular values have different importance and should be treated discriminatively, in this paper, we propose a non-convex tensor low-rank approximation (NTLA) method for infrared small target detection. In our method, NTLA adaptively assigns different weights to different singular values for accurate background estimation. Based on the proposed NTLA, we use the asymmetric spatial-temporal total variation (ASTTV) to thoroughly describe background feature, which can achieve good background estimation and detection in complex scenes. Compared with the traditional total variation approach, ASTTV exploits different smoothness strength for spatial and temporal regularization. We develop an efficient algorithm to find the optimal solution of the proposed model. Compared with some state-of-the-art methods, the proposed method achieve an improvement in different evaluation metrics. Extensive experiments on both synthetic and real data demonstrate the proposed method provide a more robust detection in complex situations with low false rates.

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Section: Dl-based Infrared Small Target Detection Methodsmentioning

confidence: 99%

Non-Convex Tensor Low-Rank Approximation for Infrared Small Target Detection

Liu,

Yang,

et al. 2021

Preprint

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show abstract

“…Note that, we employ RG as the backbone of our MoCoPnet for the following reasons: RG can generate features with large receptive field and dense sampling rate, which promotes the information exploitation. The reuse of hierarchical features not only improves the SR performance [67] but also maintains the information of small targets [1], [61], [68].…”

Section: B Central Difference Residual Groupmentioning

confidence: 99%

“…In addition, we employ Hui and Anti-UAV as the test dataset to test the robustness of our MoCoPnet to real scenes. In Anti-UAV dataset, only the sequences with infrared small target [1] (21 sequences in total) are selected as the test set. Note that, we only use the first 100 images of each sequence for test to balance computational/time cost and generalization performance.…”

Section: A Experiments Settingsmentioning

confidence: 99%

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Local Motion and Contrast Priors Driven Deep Network for Infrared Small Target Superresolution

Ying

Wang

et al. 2022

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

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Infrared small target super-resolution (SR) aims to recover reliable and detailed high-resolution image with highcontrast targets from its low-resolution counterparts. Since the infrared small target lacks color and fine structure information, it is significant to exploit the supplementary information among sequence images to enhance the target. In this paper, we propose the first infrared small target SR method named local motion and contrast prior driven deep network (MoCoPnet) to integrate the domain knowledge of infrared small target into deep network, which can mitigate the intrinsic feature scarcity of infrared small targets. Specifically, motivated by the local motion prior in the spatio-temporal dimension, we propose a local spatiotemporal attention module to perform implicit frame alignment and incorporate the local spatio-temporal information to enhance the local features (especially for small targets). Motivated by the local contrast prior in the spatial dimension, we propose a central difference residual group to incorporate the central difference convolution into the feature extraction backbone, which can achieve center-oriented gradient-aware feature extraction to further improve the target contrast. Extensive experiments have demonstrated that our method can recover accurate spatial dependency and improve the target contrast. Comparative results show that MoCoPnet can outperform the state-of-the-art video SR and single image SR methods in terms of both SR performance and target enhancement. Based on the SR results, we further investigate the influence of SR on infrared small target detection and the experimental results demonstrate that MoCoPnet promotes the detection performance. The code is available at https://github.com/XinyiYing/MoCoPnet.

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“…Du et al [ 38 ] proposed FA-YOLO with a CBAM module in the backbone to enhance the performance of infrared occlusion object detection under a confusing background. In terms of improving feature fusion strategy, Dai et al [ 39 ] proposed the asymmetric contextual modulation (ACM), which explored the fusion method between deep and shallow features. Inspired by the improved networks of UNet [ 40 , 41 , 42 , 43 ], a dense nested attention network (DNANet) [ 44 ] was proposed to resolve the detection of infrared small targets with dense nested connection and a CSAM attention mechanism.…”

Section: Related Workmentioning

confidence: 99%

CAA-YOLO: Combined-Attention-Augmented YOLO for Infrared Ocean Ships Detection

Yuan

Qian

et al. 2022

Sensors

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Infrared ocean ships detection still faces great challenges due to the low signal-to-noise ratio and low spatial resolution resulting in a severe lack of texture details for small infrared targets, as well as the distribution of the extremely multiscale ships. In this paper, we propose a CAA-YOLO to alleviate the problems. In this study, to highlight and preserve features of small targets, we apply a high-resolution feature layer (P2) to better use shallow details and the location information. In order to suppress the shallow noise of the P2 layer and further enhance the feature extraction capability, we introduce a TA module into the backbone. Moreover, we design a new feature fusion method to capture the long-range contextual information of small targets and propose a combined attention mechanism to enhance the ability of the feature fusion while suppressing the noise interference caused by the shallow feature layers. We conduct a detailed study of the algorithm based on a marine infrared dataset to verify the effectiveness of our algorithm, in which the AP and AR of small targets increase by 5.63% and 9.01%, respectively, and the mAP increases by 3.4% compared to that of YOLOv5.

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Asymmetric Contextual Modulation for Infrared Small Target Detection

Cited by 244 publications

References 31 publications

Non-Convex Tensor Low-Rank Approximation for Infrared Small Target Detection

Non-Convex Tensor Low-Rank Approximation for Infrared Small Target Detection

Local Motion and Contrast Priors Driven Deep Network for Infrared Small Target Superresolution

CAA-YOLO: Combined-Attention-Augmented YOLO for Infrared Ocean Ships Detection

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