ISTDU-Net: Infrared Small-Target Detection U-Net

Hou, Qingyu; Zhang, Liuwei; Tan, Fanjiao; Xi, Yuyang; Zheng, Haoliang; Li, Na

doi:10.1109/lgrs.2022.3141584

Cited by 70 publications

(8 citation statements)

References 11 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%

“…ALCNet [26] modularizes MPCM [32] as special blocks in the network to achieve a learnable local contrast measurement, which improves the feature extraction capability of the network. 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.…”

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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“…Hou et al [19] constructed a feature extraction framework, combining manual feature methods to build a mapping network between feature maps and small target likelihoods in images. Hou et al [20] converted a single infrared image frame into a probabilistic likelihood map of the target in terms of the image pixels, introduced feature groups into network downsampling at the perception layer, enhanced the small target feature group weights to improve the representation of small targets, and introduced a skip connection layer with full convolution. Ma et al [21] proposed a small infrared target detection network with generated labels and feature mapping to deal with the low contrast and low signal-to-noise ratio characteristics of small infrared targets.…”

Section: Introductionmentioning

confidence: 99%

AFFPN: Attention Fusion Feature Pyramid Network for Small Infrared Target Detection

et al. 2022

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The detection of small infrared targets lacking texture and shape information in the presence of complex background clutter is a challenge that has attracted considerable research attention in recent years. Typical deep learning-based target detection methods are designed with deeper network structures, which may lose targets in the deeper layers and cannot directly be used for small infrared target detection. Therefore, we designed the attention fusion feature pyramid network (AFFPN) specifically for small infrared target detection. Specifically, it consists of feature extraction and feature fusion modules. In the feature extraction stage, the global contextual prior information of small targets is first considered in the deep layer of the network using the atrous spatial pyramid pooling module. Subsequently, the spatial location and semantic information features of small infrared targets in the shallow and deep layers are adaptively enhanced by the designed attention fusion module to improve the feature representation capability of the network for targets. Finally, high-performance detection is achieved through the multilayer feature fusion mechanism. Moreover, we performed a comprehensive ablation study to evaluate the effectiveness of each component. The results demonstrate that the proposed method performs better than state-of-the-art methods on a publicly available dataset. Furthermore, AFFPN was deployed on an NVIDIA Jetson AGX Xavier development board and achieved real-time target detection, further advancing practical research and applications in the field of unmanned aerial vehicle infrared search and tracking.

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“…Many researchers have been inspired by small-object detection methods and have proposed detection models suitable for small objects. The optimized methods of these models can be categorized into spatial-temporal information fusion [15][16][17], residual/background information prediction [18,19], optimized region proposal [20,21], and multiscale information fusion [22][23][24][25]. The spatial-temporal information fusion method reduces static noise by combining adjacent frames in an infrared image sequence.…”

Section: Introductionmentioning

confidence: 99%

IRSDet: Infrared Small-Object Detection Network Based on Sparse-Skip Connection and Guide Maps

2022

Electronics

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Detecting small objects in infrared images remains a challenge because most of them lack shape and texture. In this study, we proposed an infrared small-object detection method to improve the capacity for detecting thermal objects in complex scenarios. First, a sparse-skip connection block is proposed to enhance the response of small infrared objects and suppress the background response. This block is used to construct the detection model backbone. Second, a region attention module is designed to emphasize the features of infrared small objects and suppress background regions. Finally, a batch-averaged biased classification loss function is designed to improve the accuracy of the detection model. The experimental results show that the proposed small-object detection framework significantly increases precision, recall, and F1-score, showing that, compared with the current advanced detection models for small-object detection, the proposed detection framework has better performance in infrared small-object detection under complex backgrounds. The insights gained from this study may provide new ideas for infrared small object detection and tracking.

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ISTDU-Net: Infrared Small-Target Detection U-Net

Cited by 70 publications

References 11 publications

IRSTFormer: A Hierarchical Vision Transformer for Infrared Small Target Detection

IRSTFormer: A Hierarchical Vision Transformer for Infrared Small Target Detection

AFFPN: Attention Fusion Feature Pyramid Network for Small Infrared Target Detection

IRSDet: Infrared Small-Object Detection Network Based on Sparse-Skip Connection and Guide Maps

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