ISTDet: An efficient end-to-end neural network for infrared small target detection

Ju, Moran; Luo, Jiangning; Liu, Guangqi; Luo, Haibo

doi:10.1016/j.infrared.2021.103659

Cited by 66 publications

(33 citation statements)

References 17 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%

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%

IRSTFormer: A Hierarchical Vision Transformer for Infrared Small Target Detection

Chen

Tan

2022

Remote Sensing

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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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“…In the field of infrared small target detection, a batch of research using deep learning methods has appeared. 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.…”

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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ISTDet: An efficient end-to-end neural network for infrared small target detection

Cited by 66 publications

References 17 publications

IRSTFormer: A Hierarchical Vision Transformer for Infrared Small Target Detection

IRSTFormer: A Hierarchical Vision Transformer for Infrared Small Target Detection

Infrared Small-Dim Target Detection with Transformer under Complex Backgrounds

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

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