Deep Learning-based Human Detection for UAVs with Optical and Infrared Cameras: System and Experiments

Hinzmann, Timo; Stegemann, Tobias; Cadena, Cesar; Siegwart, Roland

doi:10.48550/arxiv.2008.04197

Cited by 2 publications

(2 citation statements)

References 52 publications

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“…Additionally, we decided to check another backbone -the ResNet-101 model, which contains a more significant number of layers and will allow comparing the impact of the number of parameters on the metric values obtained during face mask detection. This model is often used for face detection (e.g., [36], [37]) for visible light recorded images as well as in the domain of thermal images -for example, for human detection (e.g., [38], [39]).…”

Section: A Adaptation Of Deep Learning Models To Face Mask Detection ...mentioning

confidence: 99%

Mask Detection and Classification in Thermal Face Images

Kowalczyk¹,

Sobotka²,

Rumiński³

2023

IEEE Access

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Face masks are recommended to reduce the transmission of many viruses, especially SARS-CoV-2. Therefore, the automatic detection of whether there is a mask on the face, what type of mask is worn, and how it is worn is an important research topic. In this work, the use of thermal imaging was considered to analyze the possibility of detecting (localizing) a mask on the face, as well as to check whether it is possible to classify the type of mask on the face. The previously proposed dataset of thermal images was extended and annotated with the description of a type of mask and a location of a mask within a face. Different deep learning models were adapted. The best model for face mask detection turned out to be the Yolov5 model in the ''nano'' version, reaching mAP higher than 97% and precision of about 95%. High accuracy was also obtained for mask type classification. The best results were obtained for the convolutional neural network model built on an autoencoder initially trained in the thermal image reconstruction problem. The pretrained encoder was used to train a classifier which achieved an accuracy of 91%.INDEX TERMS Deep neural networks, epidemic prevention, health infrastructure, mask area detection, mask type classification, thermal imaging.

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Section: A Adaptation Of Deep Learning Models To Face Mask Detection ...mentioning

confidence: 99%

Mask Detection and Classification in Thermal Face Images

Kowalczyk¹,

Sobotka²,

Rumiński³

2023

IEEE Access

View full text Add to dashboard Cite

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“…Version November 9, 2022 submitted to arXiv:2211.04293v1 [cs.CV] 8 Nov 2022 Anomaly detection methods for wilderness search and rescue have been evaluated earlier [14], and bimodal systems using a composition of visible and thermal information were already used to improve detection rates of machine learning algorithms [15,16]. However, none of the previous work considered occlusion.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Color Anomaly Detection in Multispectral Images for Synthetic Aperture Sensing

Seits

Kurmi

Bimber

2022

Eng

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In this article, we evaluate unsupervised anomaly detection methods in multispectral images obtained with a wavelength-independent synthetic aperture sensing technique called Airborne Optical Sectioning (AOS). With a focus on search and rescue missions that apply drones to locate missing or injured persons in dense forest and require real-time operation, we evaluate the runtime vs. quality of these methods. Furthermore, we show that color anomaly detection methods that normally operate in the visual range always benefit from an additional far infrared (thermal) channel. We also show that, even without additional thermal bands, the choice of color space in the visual range already has an impact on the detection results. Color spaces such as HSV and HLS have the potential to outperform the widely used RGB color space, especially when color anomaly detection is used for forest-like environments.

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Deep Learning-based Human Detection for UAVs with Optical and Infrared Cameras: System and Experiments

Cited by 2 publications

References 52 publications

Mask Detection and Classification in Thermal Face Images

Mask Detection and Classification in Thermal Face Images

Evaluation of Color Anomaly Detection in Multispectral Images for Synthetic Aperture Sensing

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