Outdoor Illumination Conditions Research Articles

An autonomous wearable biosensor powered by a perovskite solar cell.

Wearable sweat sensors can potentially be used to continuously and non-invasively monitor physicochemical biomarkers that contain information related to disease diagnostics and fitness tracking. However, the development of such autonomous sensors faces a number of challenges including achieving steady sweat extraction for continuous and prolonged monitoring, and addressing the high power demands of multifunctional and complex analysis. Here we report an autonomous wearable biosensor that is powered by a perovskite solar cell and can provide continuous and non-invasive metabolic monitoring. The device uses a flexible quasi-two-dimensional perovskite solar cell module that provides ample power under outdoor and indoor illumination conditions (power conversion efficiency exceeding 31% under indoor light illumination). We show that the wearable device can continuously collect multimodal physicochemical data - glucose, pH, sodium ions, sweat rate, and skin temperature - across indoor and outdoor physical activities for over 12 hours.

Single-layer organic photovoltaics fabricated via solution-based electrical doping of ternary bulk heterojunction films

The commercial viability of organic photovoltaics (OPVs) can be improved by simplifying their device geometry and easing fabrication complexity. Here, we demonstrate that solution-based p-type electrical doping of ternary bulk heterojunction (BHJ) films, which comprise 2 donor polymers and 1 fullerene acceptor (2D:1A), enables the realization of efficient single-layer OPVs. Systematic and detailed investigations of the optoelectronic characteristics of films with varying donor ratios, and their photovoltaic performance, demonstrate p-type electrical doping via post-process immersion into a 12-molybdophosphoric acid hydrate (PMA) solution, resulting in a reduced trap density and charge recombination without significantly changing the BHJ morphology. Furthermore, PMA doping of films comprising optimized ternary blend compositions and polyethylenimine enables the demonstration of single-layer OPVs with economic top electrode metals and a high level of performance under outdoor and indoor illumination conditions. These PMA-doped 2D:1A BHJ films are an attractive platform to reduce the efficiency-cost gap and accelerate the commercialization of OPVs for emerging applications.

Self-supervised and semi-supervised learning for road condition estimation from distributed road-side cameras

Monitoring road conditions, e.g., water build-up due to intense rainfall, plays a fundamental role in ensuring road safety while increasing resilience to the effects of climate change. Distributed cameras provide an easy and affordable alternative to instrumented weather stations, enabling diffused and capillary road monitoring. Here, we propose a deep learning-based solution to automatically detect wet road events in continuous video streams acquired by road-side surveillance cameras. Our contribution is two-fold: first, we employ a convolutional Long Short-Term Memory model (convLSTM) to detect subtle changes in the road appearance, introducing a novel temporally consistent data augmentation to increase robustness to outdoor illumination conditions. Second, we present a contrastive self-supervised framework that is uniquely tailored to surveillance camera networks. The proposed technique was validated on a large-scale dataset comprising roughly 2000 full day sequences (roughly 400K video frames, of which 300K unlabelled), acquired from several road-side cameras over a span of two years. Experimental results show the effectiveness of self-supervised and semi-supervised learning, increasing the frame classification performance (measured by the Area under the ROC curve) from 0.86 to 0.92. From the standpoint of event detection, we show that incorporating temporal features through a convLSTM model both improves the detection rate of wet road events (+ 10%) and reduces false positive alarms (- 45%). The proposed techniques could benefit also other tasks related to weather analysis from road-side and vehicle-mounted cameras.

Predicting Surface Reflectance Properties of Outdoor Scenes Under Unknown Natural Illumination.

Estimating and modeling the appearance of an object under outdoor illumination conditions is a complex process. This article addresses this problem and proposes a complete framework to predict the surface reflectance properties of outdoor scenes under unknown natural illumination. Uniquely, we recast the problem into its two constituent components involving the bidirectional reflectance distribution function incoming light and outgoing view directions: first, surface points' radiance captured in the images, and outgoing view directions are aggregated and encoded into reflectance maps, and second, a neural network trained on reflectance maps infers a low-parameter reflection model. Our model is based on phenomenological and physics-based scattering models. Experiments show that rendering with the predicted reflectance properties results in a visually similar appearance to using textures that cannot otherwise be disentangled from the reflectance properties.

Encapsulation Strategies for Highly Stable Perovskite Solar Cells under Severe Stress Testing: Damp Heat, Freezing, and Outdoor Illumination Conditions.

A key direction toward managing extrinsic instabilities in perovskite solar cells (PSCs) is encapsulation. Thus, a suitable sealing layer is required for an efficient device encapsulation, preventing moisture and oxygen ingression into the perovskite layer. In this work, a solution-based, low-cost, and commercially available bilayer structure of poly(methyl methacrylate)/styrene-butadiene (PMMA/SB) is investigated for PSCs encapsulation. Encapsulated devices retained 80% of the initial power conversion efficiency (PCE) at 85 °C temperature and 85% relative humidity after 100 h, while reference devices without SB (only PMMA) suffer from rapid and intense degradation after only 2 h, under the same condition. In addition, encapsulated devices retained 95% of the initial PCE under -15 °C freezing temperature after 6 h and retained ∼80% of the initial PCE after immersion in HCl (37%) for 90 min. Moreover, applying an additional aluminum metal sheet on the PMMA/SB protective bilayer leads to the improvement of device stability up to 500 h under outdoor illumination, retaining almost 90% of the initial PCE. Considering the urge to develop reliable, scalable, and simple encapsulation for future large-area PSCs, this work establishes solution-based bilayer encapsulation, which is applicable for flexible solar modules as well as other optoelectronic devices such as light-emitting devices and photodetectors.improvement of device stability up to 500 h under outdoor illumination, retaining almost 90% of the initial PCE. Considering the urge to develop reliable, scalable, and simple encapsulation for future large-area PSCs, this work establishes solution-based bilayer encapsulation, which is applicable for flexible solar modules as well as other optoelectronic devices such as light-emitting devices and photodetectors.

Performance of Integrated Thin-Film Silicon Solar Cell-Based Water-Splitting Devices under Varying Illumination Angles and an Estimation of Their Annual Hydrogen Production

We have investigated the influence of simulated outdoor illumination conditions on the functionality of photovoltaic-biased electrosynthetic (PV–EC) systems used for the production of hydrogen as a...

Onboard Detection-Tracking-Localization

This article investigates long-term positioning of moving objects by monocular vision of a miniature fixed-wing unmanned aerial vehicle. It is challenging to perform a real-time onboard vision processing task, due to the strict payload capacity and power budget limitations of microflying vehicles. We propose a parallel onboard architecture that explicitly decouples the long-term positioning task into iteratively operated detection, tracking, and localization. The proposed approach is eventually called onboard detection-tracking-localization , namely oDTL . The detector automatically extracts and identifies the object from image frames captured at in-flight durations. A learning-based network is constructed to improve detection accuracy and robustness against ever-changing outdoor illumination conditions and flying viewpoints. The tracker follows the object within specified region-of-interest from frame to frame with lower computing consumption. To further reduce target-losing rate, a concept of blind zone is proposed and applied, and its boundaries in sequential images are also theoretically inferred. The position estimator maps the flying vehicle pose, the image coordinates, and calibration specifications into real-world positions of the moving target. An extended Kalman filter is developed for rough position estimation, and a smooth module is introduced for the refinement of the position. Three offline comparative experiments and three online experiments have been conducted respectively to testify the real-time capability of our approach. The collected experimental results also demonstrate the feasible accuracy and robustness of the overall solution within the specified flying onboard scenarios.

Vehicle detection based on visual attention mechanism and adaboost cascade classifier in intelligent transportation systems

Robust and efficient vehicle detection is an essential task in intelligent transportation systems (ITS). Unfortunately, due to a great diversity of vehicle profiles and outdoor illumination conditions, it is a challenge to detect vehicles effectively. This paper proposes a method for high-performance vehicle detection based on visual attention mechanism and AdaBoost cascade classifier. Our method constructs the structural Haar features and extracts the features of samples using structural Haar features and trains an AdaBoost cascade classifier. Then we use the visual attention mechanism to extract the target candidate region. At last, we generate detecting sub-windows in the candidate region and discriminate them with the cascade classifier to realize vehicle detection. We compare the performance of this method against two variants, one using MB–LBP features and another using Haar features. The experimental results demonstrate satisfactory performance for the proposed method in term of training speed, detecting speed and detecting accuracy.

Angle of linear polarization images of outdoor scenes

Observations from the Ground-based Multiangle SpectroPolarimetric Imager (GroundMSPI) are used to relate angle of linear polarization (AoLP) measurements to material properties and illumination conditions in sunlit outdoor environments. GroundMSPI is a push-broom spectropolarimetric camera with an uncertainty in degree of linear polarization (DoLP) of ±0.005. This polarimetric accuracy yields useful AoLP images even when the DoLP is less than 0.02. AoLP images are reported with respect to dependency on surface texture, surface orientation, albedo, and illumination conditions. Agreement with well-known principles of polarized light scattering is illustrated, and several special cases are described. Expected observations of AoLP tangential to surface orientation and AoLP perpendicular to the scattering plane are reported. Significant changes in the AoLP are observed from common variations in outdoor illumination conditions. Also, simple variants in material properties change the dominant polarized light scattering process and thus the AoLP. Measurement examples that isolate a 90 deg AoLP flip are shown for a sunny and cloudy day as well as an object of high and low albedo.

Ensemble of adaboost cascades of 3L-LBPs classifiers for license plates detection with low quality images

Due to the plate formats and multiform outdoor illumination conditions during the image acquisition phase, it is challenging to find effective license plate detection (LPD) method. This paper aims to develop a new detection method for identifying vehicle license plates under low quality images using image processing techniques. In this research, a robust method using a large number of AdaBoost cascades with three levels pre-processing local binary patterns classifiers (3L-LBPs) are used to detect license plates (LPs) regions. The method achieves a very high accuracy for detecting LP number from one vehicle image. The proposed method was tested and trained with the images from 630 and 400 vehicles, respectively. The images involve many difficult conditions, such as low/high contrast, dusk, dirt, fogy, and distortion problems. The experimental results demonstrate very satisfactory performance for LP detection in term of speed and accuracy, and were better than the most of the existing methods. The processing time for the whole testing LPD system was about 1.63 seconds to 2 seconds. The overall probability detection, precision, and f-measurement are 98.56%, 95.9% and 97.19%, respectively; with false positive rate 5.6%.

급격한 광조건 변화 극복을 위한 영상 처리 기법 연구

A saturated region appears in an environment when changing illumination and lighting conditions suddenly. In this paper, we propose an image pre-processing method using an entropy-based metric of an image according to the change of indoor and outdoor illumination conditions. In this method, we show that two images of the same scene having different exposure values can be used to detect an over-exposed region through entropy information and restore a saturated region. The results show that our fused image is simple and practical.

Mesoporous perovskite solar cells and the role of nanoscale compact layers for remarkable all-round high efficiency under both indoor and outdoor illumination

Today poly and mono-crystalline silicon dominate the photovoltaic (PV) markets for outdoor applications. Nevertheless, there is a growing requirement for PV to be deployed in a wide variety of conditions from building-integrated, to portable electronics, to indoors for powering smart sensors, internet of things and homes. In this latter environment, other PV technologies such as amorphous silicon and dye sensitized solar cells have been the wide-spread choice to date for light harvesting. Here, we show that the perovskite solar cells (PSCs) we developed, in their mesoscopic form, incorporating both low temperature compact and mesoporous TiO2 layers, possess an outstanding combination of high power conversion efficiency (PCE) under both outdoor and indoor illumination conditions: i.e. PCE=15.9% (the highest for low temperature processed PSC in the mesoscopic form) under Standard Test Conditions (STC:1000 W/m2 with AM 1.5 Spectrum, 25 °C), and PCE=24% − 25.4% under indoor lighting (with a Maximum Power Density MPD=15.4 µW cm−2 at 200 lx and 32.6 µW cm−2 at 400 lx under compact fluorescent lamp). Our results demonstrate this technology to be exceptional for all-round performance, furthermore being manufactured via low-cost processing. These state-of-the-art high values were enabled by the development of high quality thin TiO2 layers deposited by atomic layer deposition (ALD). Furthermore, our investigation highlights the more stringent blocking behavior requirements of the compact layers under low-level light illumination compared to when the device has to operate under STC. Finally, the architecture and processes used were all carried out at low temperatures (T<150 °C) which enabled us to successfully transfer the design to plastic substrates.

Vacant parking area estimation through background subtraction and transience map analysis

A method for estimating the dimensions of non-delimited free parking areas by using a static surveillance camera is proposed. The proposed method is specially designed to tackle the main challenges of urban scenarios (multiple moving objects, outdoor illumination conditions and occlusions between vehicles) with no training. The core of this work is the temporal analysis of the video frames to detect the occupancy variation of the parking areas. Two techniques are combined: background subtraction using a mixture of Gaussians to detect and track vehicles and the creation of a transience map to detect the parking and leaving of vehicles. The authors demonstrate that the proposed method yields satisfactory estimates on three real scenarios while being a low computational cost solution that can be applied in any kind of parking area covered by a single camera.

A Fast and Robust License Plate Detection Algorithm Based on Two-stage Cascade AdaBoost

License plate detection (LPD) is one of the most important aspects of an automatic license plate recognition system. Although there have been some successful license plate recognition (LPR) methods in past decades, it is still a challenging problem because of the diversity of plate formats and outdoor illumination conditions in image acquisition. Because the accurate detection of license plates under different conditions directly affects overall recognition system accuracy, different methods have been developed for LPD systems. In this paper, we propose a license plate detection method that is rapid and robust against variation, especially variations in illumination conditions. Taking the aspects of accuracy and speed into consideration, the proposed system consists of two stages. For each stage, Haar-like features are used to compute and select features from license plate images and a cascade classifier based on the concatenation of classifiers where each classifier is trained by an AdaBoost algorithm is used to classify parts of an image within a search window as either license plate or non-license plate. And it is followed by connected component analysis (CCA) for eliminating false positives. The two stages use different image preprocessing blocks: image preprocessing without adaptive thresholding for the first stage and image preprocessing with adaptive thresholding for the second stage. The method is faster and more accurate than most existing methods used in LPD. Experimental results demonstrate that the LPD rate is 98.38% and the average computational time is 54.64 ms.

The Kingdom of Saudi Arabia Vehicle License Plate Recognition using Learning Vector Quantization Artificial Neural Network

In the today scenario technological intelligence is a higher demand after commodity even in traffic-based systems. These intelligent systems do not only help in traffic monitoring but also in commuter safety, law enforcement and commercial applications. The proposed Saudi Arabia Vehicle License plate recognition system splits into three major parts, firstly extraction of a license plate region secondly segmentation of the plate characters and lastly recognition of each character. This act is quite challenging due to the multiformity of plate formats and the nonuniform outdoor illumination conditions during image collection. In this paper recognition of the license plates is achieved by the implementation of the Learning Vector Quantization artificial neural network. Their results are based upon their completeness in the Saudi Arabia Vehicle License plate character recognition and theirs have obtained encouraging results from proposed technique.

License plate localization using a statistical analysis of Discrete Fourier Transform signal

License Plate Recognition (LPR) is a well-known problem and it has developed as a coherent framework. Research continues on the topic due to the diversity of license plates and outdoor illumination conditions which require attention. One of the most important steps in LPR is the localization part where license plates are extracted from video captured images. In this article we introduce a new approach of plate localization using a statistical analysis of Discrete Fourier Transform of the plate signal. The plate signal is represented by five statistics: strength of the signal, normalized maximum amplitude, frequency of maximum amplitude, frequency center and frequency spread. Combining with the color-based histogram thresholding, the method achieves 97.27% accuracy using plate signals from binary images. Comparative analysis is also reported.

Novel License Plate Detection Method Based on Heuristic Energy

License Plate Detection (LPD) is a key component in automatic license plate recognition system. Despite the success of License Plate Recognition (LPR) methods in the past decades, the problem is quite a challenge due to the diversity of plate formats and multiform outdoor illumination conditions during image acquisition. This paper aims at automatical detection of car license plates via image processing techniques. In this paper, we proposed a real-time and robust method for license plate detection using Heuristic Energy Map(HEM). In the vehicle image, the region of license plate contains many components or edges. We obtain the edge energy values of an image by using the box filter and search for the license plate region with high energy values. Using this energy value information or Heuristic Energy Map(HEM), we can easily detect the license plate region from vehicle image with a very high possibilities. The proposed method consists two main steps: Region of Interest (ROI) Detection and License Plate Detection. This method has better performance in speed and accuracy than the most of existing methods used for license plate detection. The proposed method can detect a license plate within 130 milliseconds and its detection rate is 99.2% on a 3.10-GHz Intel Core i3-2100(with 4.00 GB of RAM) personal computer.

Plant growth parameter estimation from sparse 3D reconstruction based on highly-textured feature points

Crop canopy spatial parameters are indicative of plant phenological growth stage and physiological condition, and their estimation is therefore of great interest for modeling and precision agriculture practices. Rapid increases in computing power have made stereovision models an attractive alternative to common single-image-based 2D methods, by allowing detailed estimation of the plant’s growth parameters regardless of imaging conditions. Models that have been proposed thus far are still limited in their application because of sensitivity to outdoor illumination conditions and the inherent difficulty in modeling complex plant shapes using only radiometric information. Assuming that not all of the plant-related pixels are essential for growth estimation, this study proposes a 3D reconstruction model that focuses on selected salient features on the plant surface, which are sufficient for obtaining growth characteristics. In addition, by introducing a hue-invariant model, the proposed algorithm shows robustness to diverse outdoor illumination conditions. The algorithm was tested under greenhouse and field conditions on corn, cotton, sunflower, tomato and black nightshade plants, from young seedlings to fully developed plant growth stages, and accurately estimated height (error ~4.5 %) and leaf cover area (error ~5 %). Furthermore, a strong correlation (r2 ~0.92) was found between the plant’s estimated volume and measured biomass, yielding an accurate biomass estimator in the validation tests (error ~4.5 %). This estimation ability remained stable while applying the model on plants with varying densities (overlapping leaves) and imaging setups where the standard 2D based analyses failed, thus showing the 3D modeling contribution to robust growth estimation models.

A Survey on License Plate Recognition Systems

License Plate Recognition (LPR) is a well known image processing technology. LPR system consists of four steps: capture the image from digital camera, pre-processing, character segmentation and character recognition. License plates are available in various styles and colors in various countries. Every country has their own license plate format. So each country develops the LPR system appropriate for the vehicle license plate format. Difficulties that the LPR systems face are the environmental and non-uniform outdoor illumination conditions. Therefore, most of the systems work under restricted environmental conditions like fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. Each LPR system use different combination of algorithms. From the papers being surveyed, it is realized that a good success rate of 93.7% is obtained by the combination of fuzzy logic for license plate detection and Self Organizing (SO) neural network for character recognition. Comparisons of different LPR systems are discussed in this paper.

Persian Vehicle License Plate Recognition Using Multiclass Adaboost

Des pite the success of License Plate Recognition (LPR) methods in the past decades, this problem is quite a challenge due to the diversity of plate formats and multiform outdoor illumination conditions during image acquisition. This paper presents a real-time and robust method for Persian license plate location and recognition. The proposed method consists of four main steps namely (I) Plate localization (II) Normalization, (III) Character segmentation, and (IV) Optical character recognition. First, all plates of the grabbed image are located rapidly and accurately using morphological operation and AdaBoost. After that, plates are normalized, and if they are skewed, then they will be aligned. In next step, convolution of each plate and a predefined binary mask is calculated, and then characters are segmented based on the obtained information. Finally, SAMME is utilized to classify extracted Persian numbers, alphabets, and words. In comparison with other methods, this system achieves high accuracy in plate localization, segmentation, and recognition. The success rate of the proposed method is 96.93% for plate localization utilizing morphological operation and AdaBoost, 98.75% for character segmentation, and 94.5% for optical character recognition utilizing SAMME. Finally, the overall accuracy of the proposed method is examined 90.45%.