Infrared small target segmentation with multiscale feature representation

Huang, Lianghu; Dai, Shaosheng; Huang, Tao; Huang, Xiangkang; Wang, Haining

doi:10.1016/j.infrared.2021.103755

Cited by 54 publications

(44 citation statements)

References 23 publications

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“…The data-driven CNN is able to learn features adaptively from images and outperforms model-driven methods for the detection of infrared small targets. According to different processing paradigms, CNN-based methods for SIRST detection can be divided into detection-based [19][20][21][22] and segmentation-based methods [12,13,[23][24][25][26][27][28][29]. The detectionbased method outputs the position and scale information of targets directly for the input image, in the same way as generic target detection algorithms, such as Faster RCNN [30] and SSD [31].…”

Section: Detection-based Infrared Small Target Detectionmentioning

confidence: 99%

“…Most of the segmentation networks use the encoder-decoder structure, with the encoder condensing the image to extract features and the decoder stretching the features to obtain the segmentation mask. The differences of these methods are reflected in model design [12,[23][24][25], feature optimization [26][27][28][29], and feature fusion [13]. Fang et al converts target segmentation into residual prediction, and the network outputs the background image [23], while training the segmentation network, TBCNet [24] adds a classification network as the semantic constraint to improve the learning ability of the network for image features.…”

Section: Segmentation-based Infrared Small Target Detectionmentioning

confidence: 99%

“…RISTDnet [27] uses the hand-crafted feature method as convolution layers with fixed parameters and places them at the beginning of the backbone network to form a segmentation network together with normal parameter learnable convolution layers. AGPCNet [28] and LSPM [29] add non-local modules to networks to perform feature refinement. Both of them use the self-attention mechanism to capture long-range dependency in images, but they are still limited by the locality of convolution layers.…”

Section: Segmentation-based Infrared Small Target Detectionmentioning

confidence: 99%

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IRSTFormer: A Hierarchical Vision Transformer for Infrared Small Target Detection

Chen

Tan

2022

Remote Sensing

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Infrared small target detection occupies an important position in the infrared search and track system. The most common size of infrared images has developed to 640×512. The field-of-view (FOV) also increases significantly. As the result, there is more interference that hinders the detection of small targets in the image. However, the traditional model-driven methods do not have the capability of feature learning, resulting in poor adaptability to various scenes. Owing to the locality of convolution kernels, recent convolutional neural networks (CNN) cannot model the long-range dependency in the image to suppress false alarms. In this paper, we propose a hierarchical vision transformer-based method for infrared small target detection in larger size and FOV images of 640×512. Specifically, we design a hierarchical overlapped small patch transformer (HOSPT), instead of the CNN, to encode multi-scale features from the single-frame image. For the decoder, a top-down feature aggregation module (TFAM) is adopted to fuse features from adjacent scales. Furthermore, after analyzing existing loss functions, a simple yet effective combination is exploited to optimize the network convergence. Compared to other state-of-the-art methods, the normalized intersection-over-union (nIoU) on our IRST640 dataset and public SIRST dataset reaches 0.856 and 0.758. The detailed ablation experiments are conducted to validate the effectiveness and reasonability of each component in the method.

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Section: Detection-based Infrared Small Target Detectionmentioning

confidence: 99%

Section: Segmentation-based Infrared Small Target Detectionmentioning

confidence: 99%

Section: Segmentation-based Infrared Small Target Detectionmentioning

confidence: 99%

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IRSTFormer: A Hierarchical Vision Transformer for Infrared Small Target Detection

Chen

Tan

2022

Remote Sensing

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“…Ju et al [5] proposed an image filtering module is proposed to obtain the confidence map, aiming to enhance the response of infrared small targets and suppress the response of the background. Huang [6] et al used multiple well-designed local similarity pyramid modules to improve the capture ability of infrared small target multi-scale features. Zhang et al [7] proposed a method to generate synthetic TIR data from RGB data in infrared tracking.…”

Section: Introductionmentioning

confidence: 99%

YOLO-SASE: An Improved YOLO Algorithm for the Small Targets Detection in Complex Backgrounds

Zhou

Jiang

et al. 2022

Sensors

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To improve the detection ability of infrared small targets in complex backgrounds, an improved detection algorithm YOLO-SASE is proposed in this paper. The algorithm is based on the YOLO detection framework and SRGAN network, taking super-resolution reconstructed images as input, combined with the SASE module, SPP module, and multi-level receptive field structure while adjusting the number of detection output layers through exploring feature weight to improve feature utilization efficiency. Compared with the original model, the accuracy and recall rate of the algorithm proposed in this paper were improved by 2% and 3%, respectively, in the experiment, and the stability of the results was significantly improved in the training process.

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“…Fang et al [21] integrated global and local dilated residual convolution blocks into U-Net for the remote infrared detection of unmanned aerial vehicles (UAVs). Huang et al [22] used multiple well-designed local similarity pyramid modules (LSPMs) and attention mechanisms for the segmentation of infrared small targets. Although recent CNN-based approaches have achieved some performance improvements, they still suffer from target loss and unclear segmentation of details.…”

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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Infrared small target segmentation with multiscale feature representation

Cited by 54 publications

References 23 publications

IRSTFormer: A Hierarchical Vision Transformer for Infrared Small Target Detection

IRSTFormer: A Hierarchical Vision Transformer for Infrared Small Target Detection

YOLO-SASE: An Improved YOLO Algorithm for the Small Targets Detection in Complex Backgrounds

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

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