Shadow Occlusion Research Articles

High-Precision Disparity Estimation for Lunar Scene Using Optimized Census Transform and Superpixel Refinement

High-precision lunar scene 3D data are essential for lunar exploration and the construction of scientific research stations. Currently, most existing data from orbital imagery offers resolutions up to 0.5–2 m, which is inadequate for tasks requiring centimeter-level precision. To overcome this, our research focuses on using in situ stereo vision systems for finer 3D reconstructions directly from the lunar surface. However, the scarcity and homogeneity of available lunar surface stereo datasets, combined with the Moon’s unique conditions—such as variable lighting from low albedo, sparse surface textures, and extensive shadow occlusions—pose significant challenges to the effectiveness of traditional stereo matching techniques. To address the dataset gap, we propose a method using Unreal Engine 4 (UE4) for high-fidelity physical simulation of lunar surface scenes, generating high-resolution images under realistic and challenging conditions. Additionally, we propose an optimized cost calculation method based on Census transform and color intensity fusion, along with a multi-level super-pixel disparity optimization, to improve matching accuracy under harsh lunar conditions. Experimental results demonstrate that the proposed method exhibits exceptional robustness and accuracy in our soon-to-be-released multi-scene lunar dataset, effectively addressing issues related to special lighting conditions, weak textures, and shadow occlusion, ultimately enhancing disparity estimation accuracy.

Optical 3D Surface Vertical Measurement Based on 2D Generalized S-Transform

Abstract The optical 3D surface measurement technique aligns the optical axis of the projector with that of the camera, enabling the measurement of objects with significant height variations, such as deep holes and grooves. This technique encodes the height of the object into the modulation of the fringe pattern, eliminating the requirement for phase unwrapping and avoiding issues such as shadow occlusion. To further enhance the noise reduction capability of fringe analysis and reconstruct objects with high-frequency detail, this paper introduces the two-dimensional generalized S-transform (2D-GST) method for modulation extraction. By incorporating two additional parameters px and py to adjust the resolution in the time domain/frequency domain, 2D-GST can provide higher reconstruction precision. The root mean square (RMS) for tested plane with 2D-GST is 4.35 μm, whereas for the traditional 1D S-transform (1D-ST), the RMS is 4.97 μm.

Integrating Ray Tracing with Single-Objective Optimization in Heliostat Field Design

Tower solar thermal power generation is a new type of low-carbon and environmentally friendly clean energy technology. Through continuous optimization of the optical efficiency of mirror field and other key technologies, the energy utilization efficiency and economic benefits of solar thermal power stations can be improved, and contribute to the sustainable development of clean energy. This paper establishes a geometric model based on the light tracing method to solve the daily average, average annual optical efficiency, thermal output power, and average optical efficiency of the heliostatic field. The cosine efficiency is the cosine of the incident light and the normal angle, which is the cosine of the incidence angle; the atmospheric transmission is only related to the distance between the mirror center and the collector center; the mirror reflection constant is 0.92. The shadow occlusion efficiency and the truncation efficiency of the collector are the difficulties in the process of calculating the optical efficiency: for the calculation of the shadow occlusion efficiency, the Monte Carlo light tracing method is adopted. Based on the energy density of the helioscope, the occlusion efficiency of the shadow can be expressed as the ratio between the amount of the outgoing light to the total generated amount; for the calculation of the collector truncation efficiency, it links the reflected light with the surface equation of the collector. According to the joint results, if there is a solution, it means that the reflected light can be reflected in the collector, and the collector truncation efficiency can represent the ratio of the number of lights reflected in the collector to the number of unblocked lights in all the reflected lines. Finally, the average annual optical efficiency and output power parameters are respectively: 0.6093, 0.7431, 0.9283, 0.9828, 35. 4430MW, 0.5642kW/m². Results for the remaining months are shown in the main text.

Research on the annual average thermal power of heliostat field in tower solar energy

The concentrating mirror field is a concentrating and heat collecting subsystem in the tower solar thermal power station, and its optical performance directly affects the solar energy utilization efficiency of the power station. In this paper, the coordinate system of heliostat field and single heliostat is established, and the grid division technology is introduced to describe its reflection effect on sunlight and its occlusion. In this paper, the shadow occlusion loss that affects the optical efficiency is divided into three parts, including incident light occlusion loss, reflected light occlusion loss and absorption tower shadow occlusion loss. For any time, the output power of the heliostat near the solar direction is small, while the output power of the heliostat far away from the solar direction is large. Compared with other influencing factors, the cosine loss has the greatest influence on the heliostat field, and the center of the absorption tower installed in the circular heliostat field is obtained. The size of the heliostat is a square with a side length of 6m, the installation height is 4m, and the annual average optical efficiency is 0.6216 when the position of the heliostat is determined. The annual average output thermal power is 36.3958 MW, and the annual average output thermal power per unit mirror area is 0.5794 .

Unsupervised fault detection approach based on depth auto-encoder for photovoltaic power generation system

Fault detection for photovoltaic power generation system is a challenging problem in condition monitoring and troubleshooting, which aims to maintain the safe operation of equipment and improve the benefit of photovoltaic industry. Aiming at the problems of frequent failures of photovoltaic power generation system, large amount of operating data and difficult to obtain fault samples, we propose an unsupervised fault detection approach for photovoltaic power generation system via bidirectional long/short memory deep auto-encoder which combines the auto-encoder in deep learning with the Bi-directional Long Short-Term Memory (BiLSTM). Specifically, We first take the statistical feature enhanced as the input of an auto-encoder based on BiLSTM. Then, we build a simulation model of Grid-connected PV system. Finally, we use the operation results under normal conditions to train the fault detection model to obtain the reconstruction error and determine the fault detection threshold, so as to judge the anomalies of the photovoltaic power generation system. We simulate the shadow occlusion fault and verify the effectiveness of the proposed method, and the fault detection accuracy of 0.95 is achieved. Compare with other models, the results show that it could set up better dependence on multi-dimensional data in time sequences, effectively testing solar panel failures and solving insufficient data labels problems.

Monte Carlo simulation-based simulation of shadow occlusion efficiency of a heliostat field

Tower solar photovoltaic power plant has a broad application prospect as a sustainable energy solution, in which the shadow occlusion efficiency, as an important part of the optical efficiency of the mirror field, has always been a computational difficulty. In this paper, the geometric projection method and Monte Carlo algorithm are used to explore the shadow occlusion efficiency of a heliostat field, and the two parts of shadowing and occlusion are simulated based on the coordinate transformation under the mirror field coordinate system. By calculating the shadow occlusion efficiency, the energy loss of the heliostat system can be evaluated, and then the system design can be optimized to improve the energy harvesting efficiency of the system.

Design of heliostat field based on ray tracing

In the tower solar thermal power generation system, the optical efficiency of mirror field is the primary factor that determines the power generation efficiency of the whole system. Given the size and installation height of heliostat, in order to meet the requirements of rated Power 60MW, the parameters of heliostat field are designed and the model of tower solar thermal power generation system is established. Considering the cosine loss, shadow occlusion loss and truncation efficiency, and knowing that these three points are closely related to the position of the Sun, a method of ray tracing is proposed to calculate the sunshine accurately, the optimum parameters of helioscope field were determined by simulation. The simulation results show that the average thermal power output per unit mirror area can be increased by reducing the shadow occlusion between the heliostats.

Research on Optimization Design Based on Heliostat Field

This study aims to deeply explore the mathematical modeling and performance optimization of solar heliostat fields, in order to improve their energy output efficiency. The solar heliostat field is an innovative solar heating system that focuses solar radiation on the absorption tower through a reflector, thereby generating high-temperature heat energy. This article mainly discusses the performance analysis and parameter optimization of the heliostat field to achieve optimal performance while meeting the rated power requirements. A simulated solar model was established using a three-dimensional Cartesian coordinate system, taking into account the effects of solar altitude angle and direct normal irradiance (DNI). The model in this paper comprehensively considers the effects of shadow occlusion loss, cosine efficiency, atmospheric transmittance, collector truncation efficiency, and mirror reflectance on the mirror field. The specific values of each parameter were calculated using model algorithms such as the HFLCAL model, To provide a basis for accurate performance analysis and solve the problem of parameter optimization, this article defines an objective function aimed at improving the energy utilization efficiency of the heliostat field by adjusting the parameters of the reflector, in order to achieve optimal performance. The above parameters are determined by establishing a genetic algorithm to define constants and initial parameters, and initializing the overall layout of the heliostat field to calculate the optical efficiency of the heliostat. In each iteration, the optimal heliostat field layout can be recorded. The final optimal solution includes parameters such as the position coordinates of the absorption tower, heliostat size, installation height, number of heliostats, and heliostat position.

Balanced Cloud Shadow Compensation Method in High-Resolution Image Combined with Multi-Level Information

As clouds of different thicknesses block sunlight, large areas of cloud shadows with varying brightness can appear on the ground. Cloud shadows in high-resolution remote sensing images lead to uneven loss of image feature information. However, cloud shadows still retain feature information, and how to compensate for and restore unbalanced cloud shadow occlusion is of great significance in improving image quality. Though traditional shadow compensation methods can enhance the shaded brightness, the results are inconsistent in a single shadow region with over-compensated or insufficient compensation problems. Thus, this paper proposes a shadow-balanced compensation method combined with multi-level information. Multi-level information comprising the information of a shadow pixel, a local super-pixel centered with the pixel, the global cloud shadow region, and the global non-shadow region information, to comply with the cloud shadow’s internal difference. First, the original image is detected via the cloud shadow detection method and post-processing. The initial shadow is detected combined with designed complex shadow features and morphological shadow index features with threshold methods. Then, post-processing considering shadow area and morphological operation is applied to remove the small, non-cloud-shadow objects. Meanwhile, the initial image is also divided into super-pixel homogeneity regions using the super-pixel segmentation principle. A super-pixel region is between the pixel and the shadow area. Different from pixel and other window regions, it can provide a different measurement levels considering object homogeneity. Thus, a balanced compensation model is designed by combining the feature value of a shadow pixel and the mean and variance of a super-pixel, shadow region, and non-shadow region on the basis of the linear correlation correction principle. The super-pixel around the shadow pixel provides a local reliable homogenous region. It can reflect the internal difference inside the shadow region. Therefore, introducing a super-pixel in the proposed model can effectively compensate for the shaded information in a balanced way. Compared to those of only using pixel and shadow region information, the compensated results introduce super-pixel information, can deal with the homogenous region as a global one, and can be adaptive to the illustration differences in a cloud shadow. The experimental results show that compared to that of other reference methods, the quality of the proposed compensation result is better. The proposed method can enhance brightness and recover detailed information in shadow regions in a more balanced way. The issue of over-compensation and insufficient compensation inside a single shadow region can be resolved. Thus, the total result is similar to that of a non-shadow region. The proposed method can be used to recover the cloud shadow information more self-adaptively to improve image quality and usage in other applications.

HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion

Road extraction from high-resolution satellite images has become a significant focus in the field of remote sensing image analysis. However, factors such as shadow occlusion and spectral confusion hinder the accuracy and consistency of road extraction in satellite images. To overcome these challenges, this paper presents a multi-scale fusion-based road extraction framework, HRU-Net, which exploits the various scales and resolutions of image features generated during the encoding and decoding processes. First, during the encoding phase, we develop a multi-scale feature fusion module with upsampling capabilities (UMR module) to capture fine details, enhancing shadowed areas and road boundaries. Next, in the decoding phase, we design a multi-feature fusion module (MPF module) to obtain multi-scale spatial information, enabling better differentiation between roads and objects with similar spectral characteristics. The network simultaneously integrates multi-scale feature information during the downsampling process, producing high-resolution feature maps through progressive cross-layer connections, thereby enabling more effective high-resolution prediction tasks. We conduct comparative experiments and quantitative evaluations of the proposed HRU-Net framework against existing algorithms (U-Net, ResNet, DeepLabV3, ResUnet, HRNet) using the Massachusetts Road Dataset. On this basis, this paper selects three network models (U-Net, HRNet, and HRU-Net) to conduct comparative experiments and quantitative evaluations on the DeepGlobe Road Dataset. The experimental results demonstrate that the HRU-Net framework outperforms its counterparts in terms of accuracy and mean intersection over union. In summary, the HRU-Net model proposed in this paper skillfully exploits information from different resolution feature maps, effectively addressing the challenges of discontinuous road extraction and reduced accuracy caused by shadow occlusion and spectral confusion factors. In complex satellite image scenarios, the model accurately extracts comprehensive road regions.

Research on route planning for solar UAV based on the intelligent optimization algorithm.

The solar unmanned aerial vehicle (UAV) route planning needs to comprehensively consider the conversion efficiency of solar cells under the influence of solar ground reflection radiation and sky scattering radiation. On the one hand, it is necessary to consider the cost of radar threat, mileage energy consumption, mountain impact and other costs. On the other hand, it is also necessary to consider the influence of high threat, mountain shadow occlusion cost, and cloud shading cost on solar photovoltaic conversion efficiency. The above problem was solved through using the ant colony intelligent optimization algorithm. By constructing ant colony paths rationally, models of mountain impact cost, high threat, mountain shadow shelter cost, and cloud shading cost were established. The constraints such as the maximum action distance, solar irradiation angle and effective action distance of various costs were introduced into the cost model and exploration factor calculation, and the comprehensive optimization problem of solar UAV route was solved. Finally, the simulation results show that the algorithm path structure is reasonable; the target node can be found independently; the convergence speed can meet the requirements of route planning; the generated route cost is small; the algorithm is reasonable and effective.

LCA and Scenario Analysis of Building Carbon Emission Reduction: The Influencing Factors of the Carbon Emission of a Photovoltaic Curtain Wall

The problem of global warming has become a major global concern, and reducing greenhouse gas emissions is crucial to mitigate its effects. Photovoltaic power generation is clean, low-carbon energy. Photovoltaic products can convert solar energy into electricity, reducing CO2 emissions to an extent. This paper introduces the life cycle evaluation theory to assess the carbon emissions of photovoltaic curtain walls. PVsyst software allows for the simulation and calculation of power generation under different influencing factors, which provides valuable information about the carbon reduction potential of photovoltaic curtain walls. The evaluation of carbon emissions and their influencing factors using grey correlation analysis further enhances the understanding of the benefits and limitations of photovoltaic curtain walls. According to the results of grey correlation analysis, this paper concludes that the degree of various influencing factors on carbon emission of a photovoltaic curtain wall under different scenarios in descending order is as follows: orientation, location, inclination, shadow occlusion, and seasonal changes. The research findings of this paper provide a theoretical reference for the future development and application of photovoltaic curtain walls. By demonstrating the carbon reduction potential of this technology, this study contributes to promoting the adoption of photovoltaic curtain walls as a sustainable solution to mitigate the effects of global warming.

Application of the MPPT Control Algorithm Based on Hybrid Quantum Particle Swarm Optimization in a Photovoltaic Power Generation System

The Maximum Power Point Tracking method is a mainstream method for improving the operational efficiency of photovoltaic power generation, but it is difficult to adapt to the rapidly changing environment and lacks good steady-state and dynamic performance. To achieve fast and accurate tracking of the Maximum Power Point Tracking, the optimization of the contraction expansion coefficient of the Quantum Particle Swarm Optimization algorithm is studied, and then the Levy flight strategy is introduced to optimize the algorithm’s global convergence ability, thereby constructing the Hybrid Quantum Particle Swarm Optimization algorithm. Finally, the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm is obtained. The research results showed that the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm can always converge to the theoretical minimum value with a probability of more than 94% in the Roserock function and Rastigin function tests. The tracking error of the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm was less than 1% under lighting conditions. The convergence time of the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm in arbitrary shadow occlusion environments can reach a stable state within 0.1 s. In summary, the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm proposed in the study has excellent performance and very wide applicability. To a certain extent, it improves the total power generation capacity of the photovoltaic power generation system and the power generation efficiency of the photovoltaic array.

A Novel Voltage Balance Topology With High Efficiency and Low Current Stress for MVdc Interface of Distributed PV

The input independent output series DC/DC converter has one stage of power conversion, which has low cost and high efficiency. However, shadow occlusion, dust coverage, and manufacturing differences will cause power mismatch of the photovoltaic (PV) panels, which in turn causes the unbalanced output voltage. In order to avoid device damage and power degradation caused by unbalanced voltage, a novel topology based on multi-winding transformer is proposed. By multiplexing the switches of each sub-module (SM), this topology can transmit the mismatched power among all the SMs and balance the output voltages. In the traditional method, the mismatched power can only be transferred between adjacent SMs. When the power mismatched SMs are far away, differential power will go through many SMs to reach the target one, causing unnecessary power losses. In the proposed topology, mismatched power can be directly transferred to the target SM, Therefore, it has higher efficiency. Moreover, all the switches have zero voltage switching (ZVS), which further improves the efficiency. 1/3 of the switches in proposed topology have lower current stress than the traditional topology. The hardware cost can be reduced. Finally, the proposed topology is verified by simulation and experiment of 1kW.

Multiscale feature fusion method for lane line detection based on time series

In order to solve lane line detection in difficult traffic conditions, such as shadow occlusion, signpost degradation, curves, and tunnels, numerous models have been proposed. However, most of the existing models conduct independent single-frame image detection, which makes it difficult to utilize the continuity of driving images and is ineffective in challenging scenes. To this end, we suggest a spatiotemporal information processing model for lane line recognition that enhances critical features. In order to properly learn the correlation between continuous images, we first employ a convolutional gated recurrent unit to process spatiotemporal driving information on the basis of U-Net. Second, the pyramid split attention (PSA) module is used to enhance or suppress the obtained feature expressions. Finally, the skip connection is used to fuse the features of different scales encoded by each stage with the features processed by PSA and gradually restore to the original image size. Experiments on the TuSimple dataset demonstrate that our model outperforms representative lane line detection networks in challenging driving scenes, with an F1-measure of up to 94.302%.

Lane detection based on IBN deep neural network and attention

Lane detection is an indispensable technology for environmental perception and is a function of autonomous vehicles. Although many researchers have applied deep learning to lane detection and achieved good results, their application scenarios were relatively simple. When a lane is blocked, lost, or met with other challenges, the accuracy of lane detection decreases tremendously. This paper proposes a novel semantic segmentation network for lane detection that includes IBN-Net, an attention module, and an encoder-decoder structure called IAED. IBN-Net improves the modelling and generalisation capability with little computational complexity. The attention module can better capture context information and improve the performance of the network. We evaluated the performance of the proposed method, which improved by 6.3% over ResNet34 on the TuSimple datasets. Tests on the CULane datasets showed that our method is at least 3% better than existing methods. Experimental results showed that the proposed method has strong robustness under complex conditions such as insufficient illumination and shadow occlusion.

Maximum power point tracking of photovoltaic array based on improved Particle Swarm Optimization Algorithm

With the vigorous development of the photovoltaic industry, how to improve the efficiency of photovoltaic power generation has become an important issue, among which partial shadow occlusion is an important reason affecting the efficiency. The efficiency of photovoltaic power generation can be effectively improved by adopting the maximum power point tracking method (MPPT), but the traditional MPPT method is not ideal in the partial shadow occlusion of the photovoltaic array. To solve this problem, this paper proposes an improved particle swarm optimization method to effectively improve the tracking efficiency of MPPT when multiple peaks appear in the photovoltaic arrays power curve (P-V) under the partial shadow. The proposed method improves the learning factor of the traditional particle swarm optimization algorithm and designs the initial position of the particles according to the characteristics of the photovoltaic array. By adding the particle elimination mechanism, the number of particles changes dynamically, and the tracking speed of the algorithm for the maximum power of the photovoltaic array is improved. Through the result of the simulation, it is not difficult to get the conclusion that the improved particle swarm optimization algorithm can effectively improve the performance of the photovoltaic system under partial shadows.

Calculating the shading reduction coefficient of photovoltaic system efficiency using the anisotropic sky scattering model

The front-row shading reduction coefficient is a key parameter used to calculate the system efficiency of a photovoltaic (PV) power station. Based on the Hay anisotropic sky scattering model, the variation rule of solar radiation intensity on the surface of the PV array during the shaded period is simulated, combined with the voltage–current characteristics of the PV modules, and the shadow occlusion operating mode of the PV array is modeled. A method for calculating the loss coefficient of front shadow occlusion based on the division of the PV cell string unit and Hay anisotropic sky scattering model is proposed. This algorithm can accurately evaluate the degree of influence of the PV array layout, wiring mode, array spacing, PV module specifications, and solar radiation on PV power station system efficiency. It provides a basis for optimizing the PV array layout, reducing system loss, and improving PV system efficiency.

Lane Detection Based on Multi-Frame Image Input

Lane detection is one of the difficulties in implementing an advanced driving assistance system. In this paper, we show that the existing single frame-based algorithm suffers from the problem of unsatisfied detection result, which is directly caused by some extremely poor road environments (such as severe shadow occlusion and severe fading). To solve the problem, this paper proposes a multi-lane detection method based on the improved U-SegNet model. In order to get better feature extraction results, the number of network layers of the U-SegNet model is deepened, and a many-to-many structure is also proposed to improve the recognition rate of the algorithm. During the training stage, continuous multiple frames are input into the RNN module to get the feature maps for feature learning and prediction. The proposed method is tested on Caltech lane marking dataset, the results show that the proposed algorithm has good robustness and real-time performance, the multi-lane marking can be better detected under most complex road conditions, and the average detecting accuracy can achieve 96.95%.

Low Resistance Hot-Spot Diagnosis and Suppression of Photovoltaic Module Based on I-U Characteristic Analysis

In the hot-spot fault of photovoltaic modules, there is a low resistance hot-spot fault caused by crystal defects, such as internal crack and PN junction failure. When the faulty area is partially shaded, it will produce severe temperature rise, accelerate the aging of battery unit, and even cause fire, which will affect the safe operation of the photovoltaic system. In this paper, the low resistance hot-spot fault endangering the safe operation of photovoltaic modules is taken as the research object; the shunt effect of equivalent low resistance caused by crystal defects under local shadow occlusion is explained by using the reverse characteristic of PN junctions of battery units, and its failure mechanism is analyzed. The three working states of the power generation system and the formation conditions of hot-spots in the process of power generation are analyzed in detail. By building a simulation model, the heating power distribution characteristics of hot-spots under different external local shadow occlusions are simulated, and finally, the fault characteristics and the fault diagnosis criterion of low resistance hot-spots are obtained. A control algorithm for low resistance hot-spot diagnosis and suppression based on I-U characteristic analysis is designed, and verified by simulation and experiment. The experimental results show that the control algorithm proposed in this paper can use the I-U characteristics of photovoltaic modules to determine whether there is a low resistance hot-spot fault, and carry out real-time control according to the judgment results. If it is judged that a low resistance hot-spot module is partially shaded, actively fixing the working point of the system near the safe voltage will protect the safety of the photovoltaic module. Otherwise, performing global MPPT will ensure the maximum power output of the system.