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A Wide and Shallow Network Tailored for Infrared Small Target Detection.

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Title: A Wide and Shallow Network Tailored for Infrared Small Target Detection.
Authors: Lu, Pengsen1,2,3 (AUTHOR), Luo, Yihan1,2,3 (AUTHOR) luo.yihan@foxmail.com, Zhang, Xinyu1,2,3 (AUTHOR), Jia, Haolong1,2,3 (AUTHOR), Xia, Shiye1,2,3 (AUTHOR), Liu, Yaqing1,2,3 (AUTHOR)
Source: Remote Sensing. Jan2026, Vol. 18 Issue 2, p307. 21p.
Subjects: Computer vision, Embedded computer systems, Artificial neural networks, Real-time computing, Signal detection
Abstract: Highlights: What are the main findings? Extremely Lightweight Model: WSNet achieves state–of–the–art efficiency with only 0.054 M parameters and 1.050 G FLOPs, making it the lightest model to date in the field of Infrared Small Target Detection (IRSTD). Wide and Shallow Architecture: Contrary to conventional deep networks, WSNet adopts a wide and shallow design, which is more suitable for infrared images that lack rich semantic information. Excessive depth leads to performance degradation in IRSTD. Superior Performance–Speed Trade–off: WSNet achieves competitive detection accuracy (e.g., highest IoU on SIRST, and best Pd on NUDT–SIRST) while offering the fastest inference speed (up to 146 FPS on GPU, 30 FPS on CPU). What is the implication of the main finding? Practical Deployment in Resource–Limited Environments: WSNet's lightweight design and real–time CPU compatibility enable its deployment in embedded systems, drones, and portable infrared devices, where computational resources are limited but low–latency detection is critical. Paradigm Shift in IRSTD Architecture Design: The success of a wide and shallow network challenges the prevailing "deeper is better" assumption in deep learning for IRSTD, encouraging the community to reconsider architecture tailoring based on domain–specific characteristics. Designing lightweight yet competitive models remains a challenging problem across the computer vision community—Infrared Small Target Detection (IRSTD) is no exception. To address this challenge, we propose WSNet, a novel model that achieves competitive performance while significantly reducing computational cost and memory consumption, without relying on deeper architectures or complex fusion mechanisms. The core innovation of WSNet lies in its extremely simple yet highly efficient network architecture, tailored to the specific demands of the IRSTD task. To the best of our knowledge, WSNet is the lightest existing model in the IRSTD field, containing only 0.054 M parameters—hundreds of times fewer than state–of–the–art alternatives—and requiring merely 1.050 G FLOPs. Extensive experiments on multiple benchmark datasets show that WSNet not only performs on par with leading methods but also delivers substantially faster inference speeds, making it highly suitable for real–time applications on embedded and resource–constrained devices. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A Wide and Shallow Network Tailored for Infrared Small Target Detection.
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  Data: Highlights: What are the main findings? Extremely Lightweight Model: WSNet achieves state–of–the–art efficiency with only 0.054 M parameters and 1.050 G FLOPs, making it the lightest model to date in the field of Infrared Small Target Detection (IRSTD). Wide and Shallow Architecture: Contrary to conventional deep networks, WSNet adopts a wide and shallow design, which is more suitable for infrared images that lack rich semantic information. Excessive depth leads to performance degradation in IRSTD. Superior Performance–Speed Trade–off: WSNet achieves competitive detection accuracy (e.g., highest IoU on SIRST, and best Pd on NUDT–SIRST) while offering the fastest inference speed (up to 146 FPS on GPU, 30 FPS on CPU). What is the implication of the main finding? Practical Deployment in Resource–Limited Environments: WSNet's lightweight design and real–time CPU compatibility enable its deployment in embedded systems, drones, and portable infrared devices, where computational resources are limited but low–latency detection is critical. Paradigm Shift in IRSTD Architecture Design: The success of a wide and shallow network challenges the prevailing "deeper is better" assumption in deep learning for IRSTD, encouraging the community to reconsider architecture tailoring based on domain–specific characteristics. Designing lightweight yet competitive models remains a challenging problem across the computer vision community—Infrared Small Target Detection (IRSTD) is no exception. To address this challenge, we propose WSNet, a novel model that achieves competitive performance while significantly reducing computational cost and memory consumption, without relying on deeper architectures or complex fusion mechanisms. The core innovation of WSNet lies in its extremely simple yet highly efficient network architecture, tailored to the specific demands of the IRSTD task. To the best of our knowledge, WSNet is the lightest existing model in the IRSTD field, containing only 0.054 M parameters—hundreds of times fewer than state–of–the–art alternatives—and requiring merely 1.050 G FLOPs. Extensive experiments on multiple benchmark datasets show that WSNet not only performs on par with leading methods but also delivers substantially faster inference speeds, making it highly suitable for real–time applications on embedded and resource–constrained devices. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Embedded computer systems
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      – SubjectFull: Artificial neural networks
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      – SubjectFull: Real-time computing
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      – SubjectFull: Signal detection
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      – TitleFull: A Wide and Shallow Network Tailored for Infrared Small Target Detection.
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              Text: Jan2026
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