A pixel-level local contrast measure for infrared small target detection

Qiu, Zhaobing; Ma, Yong; Fan, Fan; Huang, Jun; Wu, Minghui; Mei, Xiaoguang

doi:10.1016/j.dt.2021.07.002

Cited by 9 publications

(5 citation statements)

References 51 publications

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“…Finally, they applied adaptive threshold operation to separate targets. The experimental results showed that their method had low false alarm rate and high speed [14].…”

Section: Related Workmentioning

confidence: 99%

Lightweight small target detection based on aerial remote sensing images

2024

J. meas. eng.

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With the upgrading of aviation space technology, the amount of information contained in remote sensing images in the aviation is gradually increasing, and the detection technology based on small targets has developed. For lightweight small targets, pixels per unit area contain more information than large targets, and their area is too small, which is easily overlooked by conventional detection models. To enhance the attention of such algorithms, this study first introduces a Control Bus Attention Mechanism (CBAM) in the fifth generation You Only Look Once (YOLOv5) algorithm to increase the algorithm’s attention to small targets and generate optimization algorithms. Then convolutional neural network is used to mark feature pixels of the optimization algorithm, eliminate redundant information, and generate fusion algorithm, which is used to generate redundant information with high similarity when the optimization algorithm surveys pixel blocks. The novelty of this study lies in using CBAM to improve YOLOv5 algorithm. CBAM module can extract important features from images by adaptively learning the channel and spatial attention of feature maps. By weighting the channel and spatial attention of the feature map, the network can pay more attention to important features and suppress irrelevant background information. This attention mechanism can help the network better capture the characteristics of small targets and improve the accuracy and robustness of detection. Embedding CBAM module into YOLOv5 detection network can enhance the network's perception of small targets. CBAM module can improve the expressive ability and feature extraction ability of the network without increasing the complexity of the network. By introducing CBAM module, YOLOv5 can better capture the characteristics of small targets in aerial remote sensing images, and improve the detection accuracy and recall rate. Finally, the proposed fusion algorithm is used for experiments on the Tiny-Person dataset and compared with the fifth, sixth, and seventh generations of You Only Look Once. When the fusion algorithm tests the target, the classification accuracy of Sea-person is 39 %, the classification accuracy of Earth-person is 31 %, and the probability of being predicted as the background is 56 % and 67 %, respectively. And the overall accuracy of this algorithm is 0.987, which is the best among the four algorithms. The experimental results show that the fusion algorithm proposed in the study has precise positioning for lightweight small targets and can achieve good application results in aerial remote sensing images.

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“…Finally, they applied adaptive threshold operation to separate targets. The experimental results showed that their method had low false alarm rate and high speed [14].…”

Section: Related Workmentioning

confidence: 99%

Lightweight small target detection based on aerial remote sensing images

2024

J. meas. eng.

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“…However, the customized feature extraction model is difficult to adapt to different detection scenarios, while the limited feature representation capability limits further improvement of detection performance. Some other researchers have attempted to introduce more features to enhance the feature representation capability, such as scale [52], gradient [44] and weighted information [60]. However, limited by the accuracy of feature extraction, they are prone to cause more missed detections while reducing false detections.…”

Section: A Infrared Small Target Detectionmentioning

confidence: 99%

“…where µ ct denotes the average intensity of the central target area, and µ nb and σ nb denote the average intensity and STD of the neighboring background area [52], respectively. For a more comprehensive comparison of detection performance, Receiver Operating Characteristic (ROC) is also chosen as an evaluation metric.…”

Section: ) Evaluation Metricsmentioning

confidence: 99%

Robust Unsupervised Multifeature Representation for Infrared Small Target Detection

Chen,

Wu,

Zheng

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Infrared small target detection is critical to infrared search and tracking (IRST) systems. However, accurate and robust detection remains challenging due to the scarcity of target information and the complexity of clutter interference. Existing methods have some limitations in feature representation, leading to poor detection performance in complex scenes. Especially when there are sharp edges near the target or in cluster multitarget detection, the "target suppression" phenomenon tends to occur. To address this issue, we propose a robust unsupervised multi-feature representation (RUMFR) method for infrared small target detection. On the one hand, robust unsupervised spatial clustering (RUSC) is designed to improve the accuracy of feature extraction; on the other hand, pixel-level multiple feature representation is proposed to fully utilize the target detail information. Specifically, we first propose the center-weighted interclass difference measure (CWIDM) with a trilayer design for fast candidate target extraction. Note that CWIDM also guides the parameter settings of RUSC. Then, the RUSC-based model is constructed to accurately extract target features in complex scenes. By designing the parameter adaptive strategy and iterative clustering strategy, RUSC can robustly segment cluster multi-targets from complex backgrounds. Finally, RUMFR that fuses pixel-level contrast, distribution and directional gradient features is proposed for better target representation and clutter suppression. Extensive experimental results show that our method has stronger feature representation capability and achieves better detection performance than several state-of-the-art methods.

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“…Intending to further reflect the detection effect of the algorithm in this paper from a mathematical perspective, this section conducts statistical analysis on the detection rate and false alarm rate of the above algorithms, and it draws the corresponding receiver operating characteristic according to the data for corresponding analysis. Its mathematical calculation model is as follows [59]:…”

Section: Algorithm Roc Analysismentioning

confidence: 99%

Dim and Small Target Detection Based on Energy Sensing of Local Multi-Directional Gradient Information

Fan

Min

et al. 2023

Remote Sensing

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It is difficult for traditional algorithms to remove cloud edge contours in multi-cloud scenarios. In order to improve the detection ability of dim and small targets in complex edge contour scenes, this paper proposes a new dim and small target detection algorithm based on local multi-directional gradient information energy perception. Herein, based on the information difference between the target area and the background area in the four direction neighborhood blocks, an energy enhancement model for multi-directional gray aggregation (EMDGA) is constructed to preliminarily enhance the target signal. Subsequently, a local multi-directional gradient reciprocal background suppression model (LMDGR) was constructed to model the background of the image. Furthermore, this paper proposes a multi-directional gradient scale segmentation model (MDGSS) to obtain candidate target points and then combines the proposed multi-frame energy-sensing (MFESD) detection algorithm to extract the true targets from sequence images. Finally, in order to better illustrate the effect of the algorithm proposed in this paper in detecting small targets in a cloudy background, four sequence images are selected for detection. The experimental results show that the proposed algorithm can effectively suppress the edge contour of complex clouds compared with the traditional algorithm. When the false alarm rate Pf is 0.005%, the detection rate Pd is greater than 95%.

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A pixel-level local contrast measure for infrared small target detection

Cited by 9 publications

References 51 publications

Lightweight small target detection based on aerial remote sensing images

Lightweight small target detection based on aerial remote sensing images

Robust Unsupervised Multifeature Representation for Infrared Small Target Detection

Dim and Small Target Detection Based on Energy Sensing of Local Multi-Directional Gradient Information

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