Target Recognition in Infrared Circumferential Scanning System via Deep Convolutional Neural Networks

Chen, Gao

doi:10.3390/s20071922

Cited by 6 publications

(5 citation statements)

References 45 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%

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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%

“…SSD-ST [21] drops low-resolution layers and enhances high-resolution layers of SSD to adapt the detection of infrared small target. Chen et al design a two-stage network for target detection in the linear scanning IRST system [22].…”

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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“…That means they did not focus on low local SCR. In [47], they propose a deep convolutional neural network (DCNN)‐based method to realize the end‐to‐end target recognition in the IRCSS (infrared circumferential scanning system). In their network, the backbone part is ResNet50 and the feature fusion part is FPN, they just put them together without any special design, and the shallowest layer is not utilized.…”

Section: Related Workmentioning

confidence: 99%

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

Lü

et al. 2020

IET Image Processing

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For the extremely small size and low signal‐to‐clutter ratio, target detection in infrared images is still a considerable challenge. Specifically, it is very difficult to detect the point targets because there is no texture and shape information can be used. A target‐oriented shallow‐deep feature‐based detection algorithm is proposed, opening up a promising direction for convolutional neural network‐based infrared dim small target detection algorithms. To ensure that small target instances can be used correctly for networks, the effective small anchor is designed according to the shallow layer of ResNet50. To determine whether a detection result belongs to the target, the authors depend on whether the detection centre is included in the ground truth area, rather than on the Intersection Over Union overlap rate, which avoids misjudging the detection result. In this way, small targets can be trained and detected correctly through ResNet50. More importantly, the authors demonstrate that spatially finer shallow features are crucial for small target detection and that semantically stronger deep features are helpful for improving detection probability. Experimental results on simulation data sets and real data sets show that the proposed algorithm can detect the point target when the local signal‐to‐clutter ratio is approximately 1.3, displaying infinite advantage and great potentiality.

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“…Existing spatial-only and spatiotemporal methods of infrared target detection were mostly developed and tested using synthetic or real datasets of large size targets, e.g., helicopter, ships, aircraft or missiles [ 3 , 4 , 5 , 8 , 25 , 26 ]. Due to their huge size and high heat dissipation, the level of infrared energy radiated by these objects is significantly greater than small UAVs (such as an X8 drone).…”

Section: Introductionmentioning

confidence: 99%

Detecting Small Size and Minimal Thermal Signature Targets in Infrared Imagery Using Biologically Inspired Vision

Uzair

Brinkworth

Finn

2021

Sensors

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Thermal infrared imaging provides an effective sensing modality for detecting small moving objects at long range. Typical challenges that limit the efficiency and robustness of the detection performance include sensor noise, minimal target contrast and cluttered backgrounds. These issues become more challenging when the targets are of small physical size and present minimal thermal signatures. In this paper, we experimentally show that a four-stage biologically inspired vision (BIV) model of the flying insect visual system have an excellent ability to overcome these challenges simultaneously. The early two stages of the model suppress spatio-temporal clutter and enhance spatial target contrast while compressing the signal in a computationally manageable bandwidth. The later two stages provide target motion enhancement and sub-pixel motion detection capabilities. To show the superiority of the BIV target detector over existing traditional detection methods, we perform extensive experiments and performance comparisons using high bit-depth, real-world infrared image sequences of small size and minimal thermal signature targets at long ranges. Our results show that the BIV target detector significantly outperformed 10 conventional spatial-only and spatiotemporal methods for infrared small target detection. The BIV target detector resulted in over 25 dB improvement in the median signal-to-clutter-ratio over the raw input and achieved 43% better detection rate than the best performing existing method.

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Target Recognition in Infrared Circumferential Scanning System via Deep Convolutional Neural Networks

Cited by 6 publications

References 45 publications

IRSTFormer: A Hierarchical Vision Transformer for Infrared Small Target Detection

IRSTFormer: A Hierarchical Vision Transformer for Infrared Small Target Detection

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

Detecting Small Size and Minimal Thermal Signature Targets in Infrared Imagery Using Biologically Inspired Vision

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