Research and Design of an Active Light Source System for UAVs Based on Light Intensity Matching Model

In the calibration system of a particle detector, it is necessary to visually locate an active light source moving in a dark environment. The active light source has a built-in lithium-ion battery, which is wirelessly charged before working, and can work continuously for 24 hours after being charging. According to this need, this paper designs and implements the wireless charging light source system. The system consists of a wireless charging control unit and a wireless light source unit. The wireless charging control unit receives instructions from the host computer to complete the charging of the wireless light source unit, and the wireless light source unit controls the on and off of the LED drive current after receiving the instructions from the control unit. After testing, the active light source can work continuously for 24 hours under the condition of being fully charged, meeting the need of visual positioning.

Through the few numbers of formal study that shows the usability of the optical see-through augmented reality (AR) display, especially in the outdoor environment, has been affected by ambient lighting conditions and dynamic real-world backgrounds. They used painted posters which represented a real-world background to examine the effect. Under changing lighting conditions, the colour palette of the environment can also cause considerable problems. For example, specific label colours can be affected by the similarity of the selected colour scheme. Besides, the actual outdoor light source illuminates the actual objects in outdoor AR systems, while the augmented virtual objects are illuminated by a virtual light source. The colour differentials arise from these two light sources, with the actual object and virtual object being visualized as a combination of the colours induced by the two light sources. Furthermore, there is a visible colour difference between the real object and the virtual object. This paper studies the effect of the real-world backgrounds using colour blending technique on optical see-through AR user interface design. It enables AR graphics to be overlaid onto real-world backgrounds as nicely as painted posters. Colour blending effects influenced physical materials as backgrounds are almost the same as their corresponding poster backgrounds, even if each pair of colours are just a metameric match.

A new approach to high precision 3-D measuring system

Judgments of approach speed for motorcycles across different lighting levels and the effect of an improved tri-headlight configuration

In the process of target detection with active light sources as calibration objects, air scattering and air absorption cause a significant loss of light energy, resulting in distortion and fragmentation of the spot shape. Inspired by band-pass filtering, this study proposes a target detection method based on variable frame rate sampling of an active light source. It primarily adopts i) image modulation for collecting the active light source signal with a specified frequency and subtracting the background, and ii) variable frame rate sampling for further weighted average to attenuate the dynamic noise. The experimental results show that the proposed method can efficiently eliminate static background, suppress dynamic noise, and detect the target location without illumination and background requirements.

New luminescent coatings are increasingly being used in highway tunnels to address inadequate internal lighting conditions. However, there is currently a lack of scientifically reliable methods to evaluate the effectiveness of these paints in improving lighting conditions, reducing driver psychological stress, and quantifying these impacts. This study utilized new luminescent coatings to improve tunnel lighting conditions, conducting real-vehicle tests to measure drivers’ physiological parameters including pupil diameter and heart rate. It examined the mechanisms through which variations in lighting conditions within highway tunnels affect the psychological workload of drivers. A hierarchical analysis–fuzzy comprehensive evaluation (AHP-FCE) method was adopted to develop a quantitative evaluation system for highway tunnel driving psychological load. The results indicate that variations in tunnel lighting substantially influence the psychological workload experienced by drivers during operation. The new luminescent coatings effectively enhanced tunnel lighting conditions, increasing average brightness and illuminance by 37.71% and 40.95%, respectively. Following lighting improvements, pupil diameter variation rates during tunnel entry and exit phases decreased by 35.37% and 10.06%, respectively, while heart rate variation rates decreased by 12.50% and 4.36%. Quantitative analysis of driver mental load revealed a comprehensive score of 0.6230 before lighting enhancement, which decreased to 0.2702 after improvements. This research introduces an innovative integrative framework that combines physiological parameter monitoring with the AHP-FCE method to quantitatively assess the psychological workload experienced by drivers in tunnel environments. This approach addresses a significant gap in the literature concerning the quantitative relationship between tunnel lighting optimization and drivers’ psychological workload responses.

Detecting small objects in challenging lighting conditions is a critical task with broad applications in computer vision, surveillance, and medical imaging. Small objects are especially vulnerable to detection errors due to their size and the significant impact of varying lighting conditions, such as low-light environments or high glare. Furthermore, the lack of a benchmark dataset that accurately simulates real-world scenarios makes it difficult to evaluate the performance of detection models in such conditions. In the proposed paper, we address these challenges by developing a comprehensive dataset specifically designed for car logo detection under diverse and challenging lighting environments. The dataset incorporates variations in lighting, viewing angles, and object scales to replicate real-world conditions. We also introduce a detection pipeline integrating EnlightenGAN for low-light image enhancement, which preserves fine details and textures, and YOLOv7 and YOLOv8 for improved detection accuracy. By enhancing image quality and refining object detection models, our approach significantly boosts the detection of small car logos, even in challenging lighting scenarios where traditional models tend to fail. Experimental results show that YOLOv8 outperforms YOLOv7 by approximately 7%, with YOLOv8 achieving a mean Average Precision (mAP) of 73.3% on the augmented dataset compared to 68.4% for the original dataset and 67.2% for the processed images. These results validate the effectiveness of GAN-based enhancement techniques in preserving small object details and improving detection performance. Additionally, introducing a new dataset and benchmark provides a valuable resource for future research in small object detection under complex lighting conditions.

A real-time crop sensor for site specific input application is the new innovation in the field of precision agriculture. At least three different type of crop sensors,viz.. Soil Doctor, N-Senior and Green Seeker have been used in different field crops. The key advantage of all these systems is that they do not need recommendation maps. However, no published data is available on Soil Doctor adoption by farmers due to company’s aggressiveness for protective patent rights. The N-Sensor is being used mainly for wheat and other small grain crops. However, one of the key limitations of N-Sensor is ambient light source. Handheld Green Seeker sensor is the latest addition to the list of crops sensors. The active light source is a major advantage of the Green Seeker sensor. Our preliminary observations on NDVI in relation to canopy development and crop growth in sugarcane are very encouraging and we envisage a potential scope of Green Seeker optical sensor for monitoring crop growth in order to adjust timing and dose of N application for maximizing cane and sugar productivity.

A new type camera constructed by an active light source and a high frame rate imaging sensor was introduced for recording three-dimensional image of the object. The intensity of the light encoded by the compressive sensing technique and a gigahertz range carrier wave generator was illuminated the object. The imaging sensor with kilo-frame rate range synchronized with the light source was used for sampling the encoded light wave reflected from object. The waveform of the carrier wave can be reconstructed with few frames by the compressive sensing technique. Resultantly, the 3D image of the object was precisely extracted from the phase of the carrier wave in very short time with few frames.

This study evaluates the generalization capabilities of computer vision (CV) models for detecting cow locations in indoor free-stall barns, with a focus on quantifying the resources required for effective deployment across different farms where camera angles and lighting conditions vary. Given the increasing reliance of precision livestock farming (PLF) on object localization techniques to monitor livestock health and optimize resource management, understanding how to deploy CV models with minimal resources while maintaining high performance is critical. To assess generalization, we investigated three key hypotheses: (1) Model generalization is equally influenced by changes in lighting conditions and camera angles; (2) Higher model complexity guarantees better generalization performance; and (3) Fine-tuning with custom initial weights trained on related tasks enhances final performance compared to using pre-trained weights. The study utilized the newly released COws LOcalization (COLO) dataset, comprising 1,254 images and 11,818 cow instances captured from five distinct imaging settings with varying camera angles and lighting conditions. The first hypothesis was tested by training models on images from specific camera angles and evaluating performance on new angles. The second hypothesis was examined by comparing the performance of five different model sizes (e.g., YOLO11n with 2.6M parameters vs. YOLO11x with 56.9M parameters) on the same dataset. The third hypothesis was evaluated by pre-training a model on a subset of images from a specific barn and fine-tuning it on another subset from a similar setting to simulate transferring the training outcome to a different location within the same barn. Results indicate that changes in lighting conditions have a negligible impact on model generalization, whereas shifting from a top-view to a side-view perspective significantly reduces performance (P < 0.001). Model complexity does not always correlate with improved generalization, as optimal complexity depends on the dataset and task. Additionally, fine-tuning with custom initial weights benefits small, simple models but provides no significant (P > 0.05) advantage for larger models compared to training from scratch. As artificial intelligence adoption grows, alongside rising concerns about its environmental impact, this study provides practical guidelines for PLF researchers to optimize CV model deployment with minimal resource consumption. It also highlights key generalization challenges and leverages the COLO dataset to drive further research in resource-efficient livestock monitoring.

Enabling FIRA medium-sized soccer robots to recognize target objects according to color information requires that competing teams manually set the range of colors according to ambient lighting conditions prior to games. This color information is used to differentiate features of target objects, such as the ball, the goals, and the field. Constructing a color-feature model such as this is extremely time-consuming and the resulting model is unable to adapt dynamically to changes in lighting conditions. This study applied a look-up table method to execute RGB-HSV color space conversion to accelerate video processing. A particle swarm optimization (PSO) scheme was developed to detect the color-feature parameters of the target objects in the HSV color space. This enables the automatic completion of color-feature modeling and the construction of the knowledge model required by the robot for object recognition. Experiment results demonstrate that the proposed method is capable of enhancing the robustness of the robot vision system in determining changes in lighting conditions. In addition, the manpower and time required to set robot parameters prior to games were reduced significantly.

This study suggests a suitable logo design application of a three colors LED light source and flexible plastic optical fiber (POF). In this study, characteristic relevant brightness of (according to the embodiment conditions of the flexible POF for logo design) for smart clothing were analyzed through two experiments. The suitable conditions of the logo design for three colors of light source were observed in 'Experiment 1'. The angle of <TEX>$80^{\circ}$</TEX> to <TEX>$100^{\circ}$</TEX> and the length of 8cm to 16cm appeared a more suitable condition for green-colored and red-colored light sources. The angle of <TEX>$80^{\circ}$</TEX> to <TEX>$100^{\circ}$</TEX> at a length of 8cm to 12cm appeared a more suitable condition for a blue-colored light source. In 'Experiment 2', a 'Klavika' in small letter was selected as suitable logo design for the application POF. The alphabet was separated by a morpheme, which is the minimal linguistic unit. All alphabets were classified into sixteen morphemes. The luminance of fourteen morphemes (realized by the embroidery method) were measured and analyzed. Subsequently, eight morphemes appeared to show a relatively equal luminance of <TEX>$3-4cd/m^2$</TEX> in a green-colored light source, <TEX>$2-3cd/m^2$</TEX> in red-colored light source, and <TEX>$2cd/m^2$</TEX> in a blue-colored light source. Four of the fourteen morphemes, showed a 20% brighter level of luminance compared to the eight morphemes above, the cast of combination of green or red light source. This result indicates that a flexible POF with 20% less luminance are believed more suitable for four morphemes combined with a green or red light source. The results of this study provide fundamental data for further approaches to clothing logo design for the application of a flexible POF.

Accuracy of skin segmentation algorithms is highly sensitive to changes in lighting conditions. When the lighting condition in a scene is different from that in the training examples, miss-classification rate of the skin segmentation algorithms is high. Using color constancy approach we aim to compensate for skin color variations to achieve accurate skin color segmentation. Skin color constancy is realized in an unsupervised manner by using the color changes observed on a face for different illuminations to drive the model. By training on few faces of different ethnicities, our model is able to generalize the color mapping for any unseen ethnicity. The color changes observed are used to learn the color mapping from one lighting condition to the other. These mappings are represented in a low dimensional subspace to obtain basis vector fields. Using these basis vector fields we can model the nonlinear color changes to transform skin colors in arbitrary lighting conditions to a reference lighting condition. We show the proof of concept of unsupervised skin color constancy on faces from the PIE database. Skin segmentation is performed on the color compensated faces using a Skin Distribution Map (SDM), which is trained on skin colors in reference lighting condition.

Abstract. This paper addresses the significant challenge faced by robotic vision systems in detecting and sorting objects accurately under varying lighting conditions. Such variations in light can lead to decreased detection accuracy and inefficiencies in automated sorting processes. The paper employs a combination of literature review and experimental validation to investigate the effectiveness of advanced image processing techniques and control algorithms. Specifically, it explores the application of CLAHE adaptive compensation, Gaussian Blur, custom ROI, and PID controllers within a visual object sorting system to improve its robustness under diverse lighting conditions. The use of CLAHE and Gaussian Blur effectively compensates for uneven lighting, while custom ROI and PID controllers further optimize the system's response to fluctuating conditions.

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Advanced Driver Assistance System (ADAS) in combination with other active safety features like air bags etc. is gaining popularity. Vision based ADAS systems perform well under ideal lighting, illumination and environmental conditions. However, with change in illumination and other lighting related factors, the effectiveness of vision based ADAS systems tend to deteriorate. Under conditions of low light, it is therefore important to develop techniques that would offset the effects of low illumination and generate an image that appears as if it were taken under ideal lighting conditions. To accomplish this, we have developed a method, that uses local color statistics from the host image with low illumination, and enhance the same using an adaptive color transfer mechanism. By taking cues from the properties of ideal images that are saved in a database, the proposed method tends to recreate the input scene (with low illumination), into a near ideal scene, based on the database images. Visual and quantitative evaluation of our method confirms that it performs well under very low light conditions as well, where standard algorithms tend to fail.&lt;/div&gt;&lt;/div&gt;