Object Recognition under Various Lighting Conditions

“…Our method [7] needs only one reference color for estimating color transformation. First we introduce a color model and color transformation based on a light reflection model, then formulate color transformation estimation with an observed color of the reference object.…”

Section: Introductionmentioning

confidence: 99%

Online learning of color transformation for interactive object recognition under various lighting conditions

Makihara

Shirai

Shimada

2004

Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.

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“…1 for an example), which are misleading for con- Another line of research focuses on the robustness of specific high-level vision algorithms. They either train models on a large volume of real-world data [31,25,48] or rely on carefully designed task-related features [29,17].…”

Section: Introductionmentioning

confidence: 99%

Multitask AET with Orthogonal Tangent Regularity for Dark Object Detection

Cui

Qi²,

et al. 2021

2021 IEEE/CVF International Conference on Computer Vision (ICCV)

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Dark environment becomes a challenge for computer vision algorithms owing to insufficient photons and undesirable noise. To enhance object detection in a dark environment, we propose a novel multitask auto encoding transformation (MAET) model which is able to explore the intrinsic pattern behind illumination translation. In a self-supervision manner, the MAET learns the intrinsic visual structure by encoding and decoding the realistic illumination-degrading transformation considering the physical noise model and image signal processing (ISP). Based on this representation, we achieve the object detection task by decoding the bounding box coordinates and classes. To avoid the over-entanglement of two tasks, our MAET disentangles the object and degrading features by imposing an orthogonal tangent regularity. This forms a parametric manifold along which multitask predictions can be geometrically formulated by maximizing the orthogonality between the tangents along the outputs of respective tasks. Our framework can be implemented based on the mainstream object detection architecture and directly trained end-to-end using normal target detection datasets, such as VOC and COCO. We have achieved the state-of-theart performance using synthetic and real-world datasets.Code is available at https://github.com/cuiziteng/MAET.

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“…In order to investigate the influence of illumination conditions on detection algorithms, Leung et al 38 examined their detection model with the use of faster region-based convolutional neural network (RCNN) and you only look once (YOLO) algorithms by controlling the intensity from low to high to evaluate the impact of lighting conditions on the detection performance. In the study, 39 authors experimentally evaluate the detection performance of the developed object recognition model for a robot, which has the ability to bring the specified object to the user, by carrying out the tests under various lighting conditions. Though great progress in object detection has been achieved by using different algorithms, there is still an issue on the performance of these algorithms under various illumination levels especially the extreme conditions.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Development of deep learning‐based equipment heat load detection for energy demand estimation and investigation of the impact of illumination

Wei

Calautit

2020

Int J Energy Res

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As the use of equipment in office buildings increases, accurate equipment usage detection is valuable for the reduction of energy consumption and carbon emission. Using the collected equipment usage information, building energy management system (BEMS) can automatically adjust the operation of heating, ventilation, and airconditioning (HVAC) systems to meet the actual demands in different conditioned spaces in real time. Previous studies highlighted that the use of conventional control strategies in office buildings such as "static" operation schedules could cause large energy waste in particular during unoccupied hours. Common equipment load detection techniques, such as power meters and survey, are unable to provide comprehensive (location, count and heat emission) and real-time equipment information necessary for demand-driven strategies. Based on a deep learning-based equipment load detection approach which employs artificial intelligence-enabled cameras, the detection performance of the proposed approach is likely going to be affected by environmental conditions, such as the positions of cameras and lighting level, within the monitored spaces. Improper detection conditions can result in incorrect detection and lead to inaccurate building energy demand estimation. This paper aims to investigate the influences of illumination conditions on the detection accuracy of the vision-based equipment load detection approach. The work will be using AI cameras to detect equipment information under different lighting levels, employing deep learning method to analyse and generate the real-time equipment usage profiles for offices which can be inputted to the BEMS to increase the efficiency of HVAC systems. The results showed that as compared with the conventionally-scheduled HVAC systems, the HVAC system with the use of equipment usage profiles conducted by the proposed approach can achieve up to 15% reduction of energy consumption. The finding indicates that adequate illumination level contributes to the decrease of building energy demand by achieving an effective deep learning approach.

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Object Recognition under Various Lighting Conditions

Cited by 10 publications

References 13 publications

Online learning of color transformation for interactive object recognition under various lighting conditions

Online learning of color transformation for interactive object recognition under various lighting conditions

Multitask AET with Orthogonal Tangent Regularity for Dark Object Detection

Development of deep learning‐based equipment heat load detection for energy demand estimation and investigation of the impact of illumination

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