Long-Wavelength IR Imaging System to Scuba Diver Detection in Three Dimensions

Feature detection and description algorithms represent an important milestone in most computer vision applications. They have been examined from various perspectives during the last decade. However, most studies focused on their performance when used on visible band imagery. This modality suffers considerably in poor lighting conditions and notably during night-time. Infrared cameras, which noticed a considerable proliferation in recent years, offer a viable alternative in such conditions. Understanding how the building blocks of computer vision applications behave in this modality would help the community accommodating them. For this reason, we carried out a performance analysis of the most commonly used feature detectors and descriptors beyond the visible. A dataset accounting for the various challenges on these algorithms has been generated. In addition, challenges inherent to the thermal modality have been considered, notably the non-uniformity noise. A comprehensive quantitative investigation into the performance of feature detectors and descriptors is therefore presented. This study would serve to filling the gap in the literature as most analyzes have been based on visible band imagery.

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“…image registration to name a few [12,36,38,39]. The most appealing property of thermal cameras is the imagery itself.…”

mentioning

confidence: 99%

Performance Evaluation of Feature Detectors and Descriptors Beyond the Visible

Mouats

Aouf

Nam

et al. 2018

J Intell Robot Syst

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“…Details of this experiment and experimental setup are discussed in [29]. The experiment demonstrates that the observation in the LWIR band allowing the tracking of an IR signature related to a diver even in low visibility conditions and the diver operation depth up to 3 m for the experiment conditions.…”

Section: Scuba-diver Bubble Wake Detection Study In Towing Tank Amentioning

confidence: 95%

On detecting surface and sub-surface bodies in water by virtue of their surface and water column wakes

Benilov

Bunin

Dardiere

et al. 2010

2010 International WaterSide Security Conference

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This paper presents theoretical and experimental results of detection and parameterization of moving bodies and scuba divers based on their surface and water column signatures under Hudson River estuary conditions. (1) Theoretical and experimental (SIT DL Towing Tank) estimates of wake characteristics for bodies of interest are carried out.(2) The IRimages of ship wakes at the surface of the Hudson River estuary show that a surface ship wake under some natural illumination conditions has different IR-images in different wave length bands. The carried out analysis of thermal surface wakes demonstrates qualitative and quantitative consistency the Hudson River data with the theoretical estimates. The developed coupled bubble-turbulence wake theory predicts the bubblewake characteristics versus the wake source parameters. The SIT Towing Tank data demonstrate that the coupled bubbleturbulence theory is reliable. (3) The scuba diver detection is presented by results of experimental study of the wake turbulence, the bubble-wake and its surface manifestations in visual and IR bands in the SIT Towing Tank and the Hudson River. The detection, recognition and tracking in visible and infrared bands of ships and their wakes, and scuba divers, using real-time computer processing are also discussed.

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“…Currently, there are two methods for auto-focusing, namely, active and passive auto-focusing. In active auto-focusing, add-on infrared [ 11 ] or other measuring tools [ 12 ] are used to measure the distance between the camera lens and target object. Passive auto-focusing involves calculating sharpness from single images obtained by the camera.…”

Section: Introductionmentioning

confidence: 99%

Development of an Automatic Testing Platform for Aviator’s Night Vision Goggle Honeycomb Defect Inspection

Jian

Peng

2017

Sensors

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Due to the direct influence of night vision equipment availability on the safety of night-time aerial reconnaissance, maintenance needs to be carried out regularly. Unfortunately, some defects are not easy to observe or are not even detectable by human eyes. As a consequence, this study proposed a novel automatic defect detection system for aviator’s night vision imaging systems AN/AVS-6(V)1 and AN/AVS-6(V)2. An auto-focusing process consisting of a sharpness calculation and a gradient-based variable step search method is applied to achieve an automatic detection system for honeycomb defects. This work also developed a test platform for sharpness measurement. It demonstrates that the honeycomb defects can be precisely recognized and the number of the defects can also be determined automatically during the inspection. Most importantly, the proposed approach significantly reduces the time consumption, as well as human assessment error during the night vision goggle inspection procedures.

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Long-Wavelength IR Imaging System to Scuba Diver Detection in Three Dimensions

Cited by 4 publications

References 6 publications

Performance Evaluation of Feature Detectors and Descriptors Beyond the Visible

Performance Evaluation of Feature Detectors and Descriptors Beyond the Visible

On detecting surface and sub-surface bodies in water by virtue of their surface and water column wakes

Development of an Automatic Testing Platform for Aviator’s Night Vision Goggle Honeycomb Defect Inspection

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