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Lin Z, Ma Y, Ming R, Yao G, Lei Z, Zhou Q, Huang M. Deep asymmetric extraction and aggregation for infrared small target detection. Sci Rep 2023; 13:21017. [PMID: 38030740 PMCID: PMC10687261 DOI: 10.1038/s41598-023-48341-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 11/25/2023] [Indexed: 12/01/2023] Open
Abstract
Infrared small target detection is widely applied in military and civilian fields. Due to the small size of infrared targets, textural detail is missing. Common target detection methods extract semantic feature by narrowing down the feature map several times, which may lead to the small targets lost in deep layers and are not effective for infrared small target detection. To solve this problem, we propose a novel network called deep asymmetric extraction and aggregation. The network mainly consists of two processes - the vertical feature extraction and the horizontal feature aggregation, both of which are enhanced by an asymmetric attention mechanism. In the vertical process, the use of asymmetric attention mechanism combined with the reduction of down-sampling makes the small target better retained in the deep layers. Then through the horizontal process, shallow spatial feature and deep semantic feature are aggregated to further highlight the small targets while suppressing background noise. Experiments on the public datasets NUAA-SISRT, NUDT-SISRT and MDvsFA-cGan show that our proposed network outperforms the state-of-the-art methods in terms of detection accuracy and parameter efficiency.
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Affiliation(s)
- Zhongwu Lin
- School of Statistics and Mathematics, Zhejiang Gongshang University, Qiantang District, Hangzhou, 310018, China
| | - Yuhao Ma
- School of Statistics and Mathematics, Zhejiang Gongshang University, Qiantang District, Hangzhou, 310018, China
| | - Ruixing Ming
- School of Statistics and Mathematics, Zhejiang Gongshang University, Qiantang District, Hangzhou, 310018, China.
| | - Guohui Yao
- School of Statistics and Mathematics, Zhejiang Gongshang University, Qiantang District, Hangzhou, 310018, China
| | - Zhuo Lei
- School of Statistics and Mathematics, Zhejiang Gongshang University, Qiantang District, Hangzhou, 310018, China
| | - Qinghui Zhou
- School of Statistics and Mathematics, Zhejiang Gongshang University, Qiantang District, Hangzhou, 310018, China
| | - Min Huang
- School of Statistics and Mathematics, Zhejiang Gongshang University, Qiantang District, Hangzhou, 310018, China
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Mu J, Rao J, Chen R, Li F. Low-Altitude Infrared Slow-Moving Small Target Detection via Spatial-Temporal Features Measure. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22145136. [PMID: 35890816 PMCID: PMC9324641 DOI: 10.3390/s22145136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 07/04/2022] [Accepted: 07/06/2022] [Indexed: 05/27/2023]
Abstract
Robust detection of infrared slow-moving small targets is crucial in infrared search and tracking (IRST) applications such as infrared guidance and low-altitude security; however, existing methods easily cause missed detection and false alarms when detecting infrared small targets in complex low-altitude scenes. In this article, a new low-altitude slow-moving small target detection algorithm based on spatial-temporal features measure (STFM) is proposed. First, we construct a circular kernel to calculate the local grayscale difference (LGD) in a single image, which is essential to suppress low-frequency background and irregular edges in the spatial domain. Then, a short-term energy aggregation (SEA) mechanism with the accumulation of the moving target energy in multiple successive frames is proposed to enhance the dim target. Next, the spatial-temporal saliency map (STSM) is obtained by integrating the two above operations, and the candidate targets are segmented using an adaptive threshold mechanism from STSM. Finally, a long-term trajectory continuity (LTC) measurement is designed to confirm the real target and further eliminate false alarms. The SEA and LTC modules exploit the local inconsistency and the trajectory continuity of the moving small target in the temporal domain, respectively. Experimental results on six infrared image sequences containing different low-altitude scenes demonstrate the effectiveness of the proposed method, which performs better than the existing state-of-the-art methods.
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Affiliation(s)
- Jing Mu
- Key Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083, China; (J.M.); (J.R.); (R.C.)
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Junmin Rao
- Key Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083, China; (J.M.); (J.R.); (R.C.)
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ruimin Chen
- Key Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083, China; (J.M.); (J.R.); (R.C.)
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fanming Li
- Key Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083, China; (J.M.); (J.R.); (R.C.)
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
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Uzair M, Brinkworth RSA, Finn A. Detecting Small Size and Minimal Thermal Signature Targets in Infrared Imagery Using Biologically Inspired Vision. SENSORS 2021; 21:s21051812. [PMID: 33807741 PMCID: PMC7961815 DOI: 10.3390/s21051812] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 02/19/2021] [Accepted: 03/03/2021] [Indexed: 11/16/2022]
Abstract
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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Affiliation(s)
- Muhammad Uzair
- Defence and Systems Institute, UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia;
- Correspondence: ; Tel.: +61-8830-23346
| | | | - Anthony Finn
- Defence and Systems Institute, UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia;
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Du Y, Wen D, Liu G, Qiu S. Small Moving Target Recognition in Star Image with TRM. INT J PATTERN RECOGN 2021. [DOI: 10.1142/s021800142150004x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Recognition of small moving targets in space has become one of the frontier scientific researches in recent decade. Most of them focus on detection and recognition in star image with sidereal stare mode. However, in this research field, few researches are about detection and recognition in star image with track rate mode. In this paper, a novel approach is proposed to recognize the moving target in single frame by machine learning method based on elliptical characteristic extraction of star points. The technical path about recognition of moving target in space is redesigned instead of traditional processing approaches. Elliptical characteristics of each star point can be successfully extracted from single image. Machine learning can achieve the classification goal in order to make sure that all moving targets can be extracted. The experiments show that our proposed approach can have better performance in star images with different qualities.
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Affiliation(s)
- Yun Du
- Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, P. R. China
- School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Desheng Wen
- Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, P. R. China
| | - Guizhong Liu
- School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, P. R. China
| | - Shi Qiu
- Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, P. R. China
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Li B, Mo D, Song Z, Wang N, Wang R, Lin M, Li S. Feature extraction algorithm of precession target based on image length and Doppler broadening. APPLIED OPTICS 2020; 59:9098-9103. [PMID: 33104618 DOI: 10.1364/ao.401887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 09/15/2020] [Indexed: 06/11/2023]
Abstract
In space defense, utilizing the micromotion features to distinguish real targets from interfering targets and decoys is effective. Due to the imaging of the high-speed precession target by microwave radar consisting of isolated scattering centers, there are many difficulties in using inverse synthetic aperture radar (ISAR) images for feature extraction. On the other hand, the inverse synthetic aperture ladar (ISAL) image is relatively continuous because of the short wavelength of laser, and the image sequence contains information about the variation in image length and Doppler width caused by target precession, which can be used for inverse motion parameters. By establishing an observation model of the precession target and performing image processing on the obtained ISAL image at different times, the image length sequence and Doppler width sequence can be obtained. Using the ellipse fitting method to process the obtained sequence, the precession parameters of the target can be obtained. The algorithm does not require prior information such as the radius and speed of the target motion, effectively improving the practicability of the algorithm. Finally, the effectiveness of the algorithm is verified by experimental results, and the error is controlled within 2%.
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Uzair M, SA Brinkworth R, Finn A. A bio-inspired spatiotemporal contrast operator for small and low-heat-signature target detection in infrared imagery. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05206-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Shan X, Zhao D, Pan M, Wang D, Zhao L. Sea-Sky Line and its Nearby Ships Detection Based on the Motion Attitude of Visible Light Sensors. SENSORS 2019; 19:s19184004. [PMID: 31527524 PMCID: PMC6767055 DOI: 10.3390/s19184004] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Revised: 09/03/2019] [Accepted: 09/14/2019] [Indexed: 11/25/2022]
Abstract
In the maritime scene, visible light sensors installed on ships have difficulty accurately detecting the sea–sky line (SSL) and its nearby ships due to complex environments and six-degrees-of-freedom movement. Aimed at this problem, this paper combines the camera and inertial sensor data, and proposes a novel maritime target detection algorithm based on camera motion attitude. The algorithm mainly includes three steps, namely, SSL estimation, SSL detection, and target saliency detection. Firstly, we constructed the camera motion attitude model by analyzing the camera’s six-degrees-of-freedom motion at sea, estimated the candidate region (CR) of the SSL, then applied the improved edge detection algorithm and the straight-line fitting algorithm to extract the optimal SSL in the CR. Finally, in the region of ship detection (ROSD), an improved visual saliency detection algorithm was applied to extract the target ships. In the experiment, we constructed SSL and its nearby ship detection dataset that matches the camera’s motion attitude data by real ship shooting, and verified the effectiveness of each model in the algorithm through comparative experiments. Experimental results show that compared with the other maritime target detection algorithm, the proposed algorithm achieves a higher detection accuracy in the detection of the SSL and its nearby ships, and provides reliable technical support for the visual development of unmanned ships.
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Affiliation(s)
- Xiongfei Shan
- Navigation College, Dalian Maritime University, Dalian 116026, China.
- Vessel Navigation System National Engineering Research Center, Dalian Maritime University, Dalian 116026, China.
| | - Depeng Zhao
- Navigation College, Dalian Maritime University, Dalian 116026, China.
- Vessel Navigation System National Engineering Research Center, Dalian Maritime University, Dalian 116026, China.
| | - Mingyang Pan
- Navigation College, Dalian Maritime University, Dalian 116026, China.
- Vessel Navigation System National Engineering Research Center, Dalian Maritime University, Dalian 116026, China.
| | - Deqiang Wang
- Navigation College, Dalian Maritime University, Dalian 116026, China.
- Vessel Navigation System National Engineering Research Center, Dalian Maritime University, Dalian 116026, China.
| | - Lining Zhao
- Navigation College, Dalian Maritime University, Dalian 116026, China.
- Vessel Navigation System National Engineering Research Center, Dalian Maritime University, Dalian 116026, China.
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Double Weight-Based SAR and Infrared Sensor Fusion for Automatic Ground Target Recognition with Deep Learning. REMOTE SENSING 2018. [DOI: 10.3390/rs10010072] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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9
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Robust Small Target Co-Detection from Airborne Infrared Image Sequences. SENSORS 2017; 17:s17102242. [PMID: 28961206 PMCID: PMC5677333 DOI: 10.3390/s17102242] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Revised: 09/17/2017] [Accepted: 09/25/2017] [Indexed: 11/17/2022]
Abstract
In this paper, a novel infrared target co-detection model combining the self-correlation features of backgrounds and the commonality features of targets in the spatio-temporal domain is proposed to detect small targets in a sequence of infrared images with complex backgrounds. Firstly, a dense target extraction model based on nonlinear weights is proposed, which can better suppress background of images and enhance small targets than weights of singular values. Secondly, a sparse target extraction model based on entry-wise weighted robust principal component analysis is proposed. The entry-wise weight adaptively incorporates structural prior in terms of local weighted entropy, thus, it can extract real targets accurately and suppress background clutters efficiently. Finally, the commonality of targets in the spatio-temporal domain are used to construct target refinement model for false alarms suppression and target confirmation. Since real targets could appear in both of the dense and sparse reconstruction maps of a single frame, and form trajectories after tracklet association of consecutive frames, the location correlation of the dense and sparse reconstruction maps for a single frame and tracklet association of the location correlation maps for successive frames have strong ability to discriminate between small targets and background clutters. Experimental results demonstrate that the proposed small target co-detection method can not only suppress background clutters effectively, but also detect targets accurately even if with target-like interference.
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10
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Automatic Counting of Large Mammals from Very High Resolution Panchromatic Satellite Imagery. REMOTE SENSING 2017. [DOI: 10.3390/rs9090878] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Li Y, Zhang Y, Yu JG, Tan Y, Tian J, Ma J. A novel spatio-temporal saliency approach for robust dim moving target detection from airborne infrared image sequences. Inf Sci (N Y) 2016. [DOI: 10.1016/j.ins.2016.07.042] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Kim S, Song WJ, Kim SH. Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection. SENSORS 2016; 16:s16071117. [PMID: 27447635 PMCID: PMC4970160 DOI: 10.3390/s16071117] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2016] [Revised: 06/22/2016] [Accepted: 07/13/2016] [Indexed: 11/16/2022]
Abstract
Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated by OKTAL-SE.
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Affiliation(s)
- Sungho Kim
- Department of Electronic Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan, Gyeongbuk 38541, Korea.
| | - Woo-Jin Song
- Department of Electrical Engineering, Pohang University of Science and Technology, Pohang, Gyeongbuk 37673, Korea.
| | - So-Hyun Kim
- Agency for Defense Development, 111 Sunam-dong, Daejeon 34186, Korea.
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Labeled RFS-Based Track-Before-Detect for Multiple Maneuvering Targets in the Infrared Focal Plane Array. SENSORS 2015; 15:30839-55. [PMID: 26670234 PMCID: PMC4721749 DOI: 10.3390/s151229829] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2015] [Revised: 11/26/2015] [Accepted: 12/01/2015] [Indexed: 11/17/2022]
Abstract
The problem of jointly detecting and tracking multiple targets from the raw observations of an infrared focal plane array is a challenging task, especially for the case with uncertain target dynamics. In this paper a multi-model labeled multi-Bernoulli (MM-LMB) track-before-detect method is proposed within the labeled random finite sets (RFS) framework. The proposed track-before-detect method consists of two parts-MM-LMB filter and MM-LMB smoother. For the MM-LMB filter, original LMB filter is applied to track-before-detect based on target and measurement models, and is integrated with the interacting multiple models (IMM) approach to accommodate the uncertainty of target dynamics. For the MM-LMB smoother, taking advantage of the track labels and posterior model transition probability, the single-model single-target smoother is extended to a multi-model multi-target smoother. A Sequential Monte Carlo approach is also presented to implement the proposed method. Simulation results show the proposed method can effectively achieve tracking continuity for multiple maneuvering targets. In addition, compared with the forward filtering alone, our method is more robust due to its combination of forward filtering and backward smoothing.
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Kim S. Sea-Based Infrared Scene Interpretation by Background Type Classification and Coastal Region Detection for Small Target Detection. SENSORS 2015; 15:24487-513. [PMID: 26404308 PMCID: PMC4610493 DOI: 10.3390/s150924487] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2015] [Accepted: 09/17/2015] [Indexed: 11/18/2022]
Abstract
Sea-based infrared search and track (IRST) is important for homeland security by detecting missiles and asymmetric boats. This paper proposes a novel scheme to interpret various infrared scenes by classifying the infrared background types and detecting the coastal regions in omni-directional images. The background type or region-selective small infrared target detector should be deployed to maximize the detection rate and to minimize the number of false alarms. A spatial filter-based small target detector is suitable for identifying stationary incoming targets in remote sea areas with sky only. Many false detections can occur if there is an image sector containing a coastal region, due to ground clutter and the difficulty in finding true targets using the same spatial filter-based detector. A temporal filter-based detector was used to handle these problems. Therefore, the scene type and coastal region information is critical to the success of IRST in real-world applications. In this paper, the infrared scene type was determined using the relationships between the sensor line-of-sight (LOS) and a horizontal line in an image. The proposed coastal region detector can be activated if the background type of the probing sector is determined to be a coastal region. Coastal regions can be detected by fusing the region map and curve map. The experimental results on real infrared images highlight the feasibility of the proposed sea-based scene interpretation. In addition, the effects of the proposed scheme were analyzed further by applying region-adaptive small target detection.
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Affiliation(s)
- Sungho Kim
- Department of Electronic Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan, Gyeongbuk 38541, Korea.
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