Target attribute-based false alarm rejection in small infrared target detection

Kim, Sungho

doi:10.1117/12.973766

Cited by 4 publications

(3 citation statements)

References 17 publications

(20 reference statements)

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“…Regarding the key parameter K in Formulas ( 2) and ( 3), achieving multi-scale target detection requires self-adaptive adjustment based on the target's size. By applying Formulas (1) to (13), the WRDLCM can be determined, and the WRDLCM values at different scales can be maximized. The formula is as follows:…”

Section: Multi-scale Wrdlcm Calculationmentioning

confidence: 99%

“…The target may only occupy a couple of pixels, making it difficult to tell apart from the background [12]. (c) Complex background edges and high brightness noise with single pixels in the image can often resemble small targets, leading to false positive detection results [13]. Therefore, achieving accurate real-time detection of small IR targets continues to be a challenging task.…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED: An Infrared Small Target Detection Method Based on a Weighted Human Visual Comparison Mechanism for Safety Monitoring

Chen,

Wang,

Pang

et al. 2023

Remote Sensing

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Infrared small target detection is a crucial technology in both military and civilian applications, including surveillance, security, defense, and combat. However, accurate infrared detection of small targets in real-time is challenging due to their small size and similarity in gray level and texture with the surrounding environment, as well as interference from the infrared imaging systems in unmanned aerial vehicles (UAVs). This article proposes a weighted local contrast method based on the contrast mechanism of the human visual system. Initially, a combined contrast ratio is defined that stems from the pixel-level divergence between the target and its neighboring pixels. Then, an improved regional intensity level is used to establish a weight function with the concept of ratio difference combination, which can effectively suppress complex backgrounds and random noise. In the final step, the contrast and weight functions are combined to create the final weighted local contrast method (WRDLCM). This method does not require any preconditioning and can enhance the target while suppressing background interference. Additionally, it is capable of detecting small targets even when their scale changes. In the experimental section, our algorithm was compared with some popular methods, and the experimental findings indicated that our method showed strong detection capability based on the commonly used performance indicators of the ROC curve, SCRG, and BSF, especially in low signal-to-noise ratio situations. In addition, unlike deep learning, this method is appropriate for small sample sizes and is easy to implement on FPGA hardware.

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Section: Multi-scale Wrdlcm Calculationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RETRACTED: An Infrared Small Target Detection Method Based on a Weighted Human Visual Comparison Mechanism for Safety Monitoring

Chen,

Wang,

Pang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…These backgrounds often have higher brightness than the target and contain more complex edge clutter information [5]. Furthermore, device manufacturing defects and random electrical noise during system operation can introduce high-brightness point noise into the original image [6]. All these factors pose challenges to accurate target detection, particularly in complex scenes involving small objects.…”

Section: Introductionmentioning

confidence: 99%

Infrared Moving Small Target Detection Based on Space–Time Combination in Complex Scenes

Wang,

Cao,

et al. 2023

Remote Sensing

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In the infrared small target images with complex backgrounds, there exist various interferences that share similar characteristics with the target (such as building edges). The accurate detection of small targets is crucial in applications involving infrared search and tracking. However, traditional detection methods based on small target feature detection in a single frame image may result in higher error rates due to insufficient features. Therefore, in this paper, we propose an infrared moving object detection method that integrates spatio-temporal information. To address the limitations of single-frame detection, we introduce a temporal sequence of images to suppress false alarms caused by single-frame detection through analyzing motion features within the sequence. Firstly, based on spatial feature detection, we propose a multi-scale layered contrast feature (MLCF) filtering for preliminary target extraction. Secondly, we utilize the spatio-temporal context (STC) as a feature to track the image sequence point by point, obtaining global motion features. Statistical characteristics are calculated to obtain motion vector data that correspond to abnormal motion, enabling the accurate localization of moving targets. Finally, by combining spatial and temporal features, we determine the precise positions of the targets. The effectiveness of our method is evaluated using a real infrared dataset. Through analysis of the experimental results, our approach demonstrates stronger background suppression capabilities and lower false alarm rates compared to other existing methods. Moreover, our detection rate is similar or even superior to these algorithms, providing further evidence of the efficacy of our algorithm.

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Joint detection and tracking of size-varying infrared targets based on block-wise sparse decomposition

Lin

Long

et al. 2016

Infrared Physics & Technology

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Target attribute-based false alarm rejection in small infrared target detection

Cited by 4 publications

References 17 publications

RETRACTED: An Infrared Small Target Detection Method Based on a Weighted Human Visual Comparison Mechanism for Safety Monitoring

RETRACTED: An Infrared Small Target Detection Method Based on a Weighted Human Visual Comparison Mechanism for Safety Monitoring

Infrared Moving Small Target Detection Based on Space–Time Combination in Complex Scenes

Joint detection and tracking of size-varying infrared targets based on block-wise sparse decomposition

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