Estimating Insect Body Size From Radar Observations Using Feature Selection and Machine Learning

Hu, Cheng; Zhang, Fan; Li, W.; Wang, Rui; Yu, Teng

doi:10.1109/tgrs.2022.3224618

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Intrusion detection systems usually include two key steps: feature selection/extraction and classification [ 25 ]. There are various methods for feature selection, such as machine learning [ 26 ] and deep learning [ 27 ]. The main goal of feature selection is to select the most important features related to intrusion and remove unnecessary elements from a vast dataset [ 28 ] to improve the detection efficiency and reduce the dimensionality of the dataset, which in turn reduces the complexity and computational cost of the intrusion detection system [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

GSOOA-1DDRSN: Network traffic anomaly detection based on deep residual shrinkage networks

Zuo,

Zhang,

et al. 2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

GSOOA-1DDRSN: Network traffic anomaly detection based on deep residual shrinkage networks

Zuo,

Zhang,

et al. 2024

Heliyon

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“…As visual information alone is often insufficient to accurately estimate size, it is common practise to include absolute scale information in the images [1,3,7,9], for example in form of reference objects of known size. Radar, sonar, or infrared light, can help address the same problem, because weight can then be estimated from coarse 3D object reconstructions [10][11][12]. Completely reference-free size-estimation, however, is rare [6].…”

Section: Introductionmentioning

confidence: 99%

WOLO: Wilson Only Looks Once – Estimating ant body mass from reference-free images using deep convolutional neural networks

Plum,

Bischoff

et al. 2024

Preprint

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Size estimation is a hard computer vision problem with widespread applications in quality control in manufacturing and processing plants, livestock management, and studies on animal behaviour. Typically, image-based size estimation is facilitated by either well-controlled imaging conditions, the provision of global cues, or both. Reference-free size estimation is challenging, because objects of vastly different sizes can appear identical if they are of similar shape. Here, we attempt to implement automated and reference-free body size estimation to facilitate large-scale experimental work in a key model species in sociobiology: the leaf-cutter ants. Leaf-cutter ants are a suitable testbed for reference-free size-estimation, because their workers differ vastly in both size and shape; in principle, it is therefore possible to infer body mass, a proxy for size, from relative body proportions alone. Inspired by earlier work by E.O. Wilson, who trained himself to discern ant worker size from visual cues alone, we used various deep learning techniques to achieve the same feat automatically, quickly, and at scale from a single reference image: Wilson Only Looks Once (WOLO). Utilizing over 3 million hand-annotated and computer-generated images, a set of deep neural networks including regressors, classifiers, and detectors were trained to estimate the body mass of ants from image cut-outs. The WOLO networks approximately matched human performance, measured for a small group of both experts and non-experts, but were about 1000 times faster. Further refinement may enable accurate, high-throughput, and non-intrusive body weight estimation at scale, and so eventually contribute to a more nuanced and comprehensive understanding of the complex division of labour that characterises polymorphic insect societies.

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Extracting Bird and Insect Migration Echoes From Single-Polarization Weather Radar Data Using Semi-Supervised Learning

Sun,

Hu,

Cui

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Estimating Insect Body Size From Radar Observations Using Feature Selection and Machine Learning

Cited by 4 publications

References 26 publications

GSOOA-1DDRSN: Network traffic anomaly detection based on deep residual shrinkage networks

GSOOA-1DDRSN: Network traffic anomaly detection based on deep residual shrinkage networks

WOLO: Wilson Only Looks Once – Estimating ant body mass from reference-free images using deep convolutional neural networks

Extracting Bird and Insect Migration Echoes From Single-Polarization Weather Radar Data Using Semi-Supervised Learning

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