Low-cost Camera System Research Articles

A Novel Cost-Effective Approach To Flow Visualization Using Particle Streak Angles

Comprehending the complexity of pipe flow dynamics is important for a variety of sectors, including environmental, industrial, and engineering research. Conventional techniques such as Particle Image Velocimetry (PIV) and Laser Doppler Velocimetry (LDV) have long been the heart of these kinds of investigations, but they are often quite expensive and complex. Our research offers a novel, affordable visualization method that aims to transform the way we examine and interpret flow dynamics in response to these challenges. Our method is developed to utilize a low-cost camera system with a simple laser setup to investigate fluid dynamics inside pipes. By introducing particles into the flow and manipulating the camera shutter speed in real-time under the laser, we created streaks capable of displaying complex flow patterns. We gathered precise information about streak angles using rigorous image processing, providing clarity in our insights into the flow dynamics of the particulate pipe flow. Notably, our strategy eliminates the need for expensive and sophisticated equipment while providing researchers and practitioners with a simple yet reliable alternative. The potential for improving our knowledge of pipe flow dynamics is shown by preliminary studies that demonstrate how well it captures dynamic flow characteristics. Our technique, which is easy to use and effective, promises fresh insights into fluid dynamics and opens up new avenues for pipe flow analysis, making it suitable for a broad spectrum of users.

High-detail and low-cost underwater inspection of large-scale hydropower dams

Abstract. The article presents a practical method that combines low-cost camera systems with remotely operated vehicles (ROVs) to accomplish a comprehensive but economically feasible underwater survey of large hydropower infrastructures. Typically, inspecting reservoirs entails draining them off to allow for visual inspections, which are time-intensive, pose risks to operators' safety and are associated with generation losses. In this regard, ROVs are a much safer and more efficient alternative to traditional methods. The study was conducted at the Pack reservoir in Austria, where a reference framework was set up using terrestrial laser scanning and checkerboard markings for the above-water components. A ROV equipped with a GoPro camera and lighting system for the underwater recordings has been employed. Via a close-range photogrammetric approach, it was possible to generate 3D point clouds of the submerged infrastructure with a survey-grade accuracy level. Various strategies were explored to perform bundle block adjustment (BBA), among these were strategies where ground control points (GCPs) were used, strategies without the use of GCPs but pre-calibrated initial camera parameters and strategies with a combination of using both GCPs and pre-calibrated camera parameters in the BBA. The deployment of an inspection technique using low-cost sensors that can generate highly detailed three-dimensional models of submerged infrastructure areas is presented and discussed, allowing easy detection and localization for maintenance inspection, all while being cost-effective. The paper strengthens the suggestion of best practices that optimize camera settings, considering the effect of electronic image stabilization, suggesting its avoidance, and using advanced calibration methods.

Analysis of the Drinking Behavior of Beef Cattle Using Computer Vision.

Monitoring the drinking behavior of animals can provide important information for livestock farming, including the health and well-being of the animals. Measuring drinking time is labor-demanding and, thus, it is still a challenge in most livestock production systems. Computer vision technology using a low-cost camera system can be useful in overcoming this issue. The aim of this research was to develop a computer vision system for monitoring beef cattle drinking behavior. A data acquisition system, including an RGB camera and an ultrasonic sensor, was developed to record beef cattle drinking actions. We developed an algorithm for tracking the beef cattle's key body parts, such as head-ear-neck position, using a state-of-the-art deep learning architecture DeepLabCut. The extracted key points were analyzed using a long short-term memory (LSTM) model to classify drinking and non-drinking periods. A total of 70 videos were used to train and test the model and 8 videos were used for validation purposes. During the testing, the model achieved 97.35% accuracy. The results of this study will guide us to meet immediate needs and expand farmers' capability in monitoring animal health and well-being by identifying drinking behavior.

Learning-Based Near-Infrared Band Simulation with Applications on Large-Scale Landcover Classification.

Multispectral sensors are important instruments for Earth observation. In remote sensing applications, the near-infrared (NIR) band, together with the visible spectrum (RGB), provide abundant information about ground objects. However, the NIR band is typically not available on low-cost camera systems, which presents challenges for the vegetation extraction. To this end, this paper presents a conditional generative adversarial network (cGAN) method to simulate the NIR band from RGB bands of Sentinel-2 multispectral data. We adapt a robust loss function and a structural similarity index loss (SSIM) in addition to the GAN loss to improve the model performance. With 45,529 multi-seasonal test images across the globe, the simulated NIR band had a mean absolute error of 0.02378 and an SSIM of 89.98%. A rule-based landcover classification using the simulated normalized difference vegetation index (NDVI) achieved a Jaccard score of 89.50%. The evaluation metrics demonstrated the versatility of the learning-based paradigm in remote sensing applications. Our simulation approach is flexible and can be easily adapted to other spectral bands.

A Low-Cost Drive and Detection Scheme for Electrowetting Display

The electrowetting display (EWD) has obtained much attention as its readability in sunlight and flexible displays. Oil motion control is an important factor for the display performance of EWD. In this paper, we propose a low-cost drive and detection scheme for EWD. The dynamic drive and detection scheme for EWD consists of a low-cost camera, computer and graphical detection system, and portable driving control system. The proposed scheme can detect oil leaking, splitting, and non-recovered defects successfully. Moreover, surface defects such as the hydrophobic layer burned and scratch can also be captured and analyzed by the proposed scheme. We hope that this scheme can provide a drive and detection platform for other EWD researchers.

Effect of low-cost transcranial magnetic stimulation navigation on hotspot targeting and motor evoked potential variability in the biceps brachii.

Transcranial magnetic stimulation (TMS) can monitor or modulate brain excitability. However, reliability of TMS outcomes depends on consistent coil placement during stimulation. Neuronavigated TMS systems can address this issue, but their cost limits their use outside of specialist research environments. The objective was to evaluate the performance of a low-cost navigated TMS approach in improving coil placement consistency and its effect on motor evoked potentials (MEPs) when targeting the biceps brachii at rest and during voluntary contractions. We implemented a navigated TMS system using a low-cost 3D camera system and open-source software environment programmed using the Unity 3D engine. MEPs were collected from the biceps brachii at rest and during voluntary contractions across two sessions in ten non-disabled individuals. Motor hotspots were recorded and targeted via two conditions: navigated and conventional. The low-cost navigated TMS system reduced coil orientation error (pitch: 1.18°±1.2°, yaw: 1.99°±1.9°, roll: 1.18°±2.2° with navigation, versus pitch: 3.7°±5.7°, yaw: 3.11°±3.1°, roll: 3.8°±9.1° with conventional). The improvement in coil orientation had no effect on MEP amplitudes and variability. The low-cost system is a suitable alternative to expensive systems in tracking the motor hotspot between sessions and quantifying the error in coil placement when delivering TMS. Biceps MEP variability reflects physiological variability across a range of voluntary efforts, that can be captured equally well with navigated or conventional approaches of coil locating.

Plant Leaf Detection and Counting in a Greenhouse during Day and Nighttime Using a Raspberry Pi NoIR Camera.

A non-destructive method using machine vision is an effective way to monitor plant growth. However, due to the lighting changes and complicated backgrounds in outdoor environments, this becomes a challenging task. In this paper, a low-cost camera system using an NoIR (no infrared filter) camera and a Raspberry Pi module is employed to detect and count the leaves of Ramie plants in a greenhouse. An infrared camera captures the images of leaves during the day and nighttime for a precise evaluation. The infrared images allow Otsu thresholding to be used for efficient leaf detection. A combination of numbers of thresholds is introduced to increase the detection performance. Two approaches, consisting of static images and image sequence methods are proposed. A watershed algorithm is then employed to separate the leaves of a plant. The experimental results show that the proposed leaf detection using static images achieves high recall, precision, and F1 score of 0.9310, 0.9053, and 0.9167, respectively, with an execution time of 551 ms. The strategy of using sequences of images increases the performances to 0.9619, 0.9505, and 0.9530, respectively, with an execution time of 516.30 ms. The proposed leaf counting achieves a difference in count (DiC) and absolute DiC (ABS_DiC) of 2.02 and 2.23, respectively, with an execution time of 545.41 ms. Moreover, the proposed method is evaluated using the benchmark image datasets, and shows that the foreground–background dice (FBD), DiC, and ABS_DIC are all within the average values of the existing techniques. The results suggest that the proposed system provides a promising method for real-time implementation.

Using Haar-like Features and SVM Classifier for Quality Assurance in a Surgical Mask Production Line

With the recent increase for demand of surgical masks, the design and development of mask production lines has become an ever pressing issue. These production lines produce low cost high quantity products. As there are errors during the production, it is important to be able to detect invalid masks to assure that the produced masks are of consistent quality. Manual quality assurance using human operators is an error prone and a costly solution. In this article we describe an image classification method, which is using a low-cost Commercial Camera System and relies on Haar-like features combined with Maximum Relevance, Minimum Redundancy feature selection to detect the invalid masks at the end of the production process. The classification method consists of Preprocessing, Feature Selection and SVM Training. We have tested the method on a database of 150 000 images and it provides a high accuracy method which we use in the Production Line.

A framework for multiscale intertidal sandflat mapping: A case study in the Whangateau estuary

We introduce a multiscale approach to population estimation of maldanid polychaetes on intertidal sandflats. Animals that live in marine sediments play important roles in ecosystem processes and are frequently the target of environmental monitoring but are notoriously time consuming to sample. Our goal was to enable the mapping of patterns of abundance of estuary lifeforms over long tracks at spatial scales and temporal frequencies not amenable by traditional quantitative methods. At each of four study sites of 9 m2, we used several forms of imagery at increasing resolution, obtained with a low-cost camera system and UAVs, to estimate the presence of the maldanid polychaete macroclymenella stewartensis. This involved defining the micro-scale topographic features on the sediment surface linked to the worms feeding activity. At each site, counts of polychate faecal mounds were estimated by quadrat counts. High resolution imagery was used to construct 3D depth maps using Structure from Motion, which were then segmented to obtain a local count of M.stewartensis faecal mounds. Texture features were then extracted from the high resolution imagery and used to create a linear model using the local counts. Low resolution imagery was then scaled, texture features extracted and passed into the linear model to provide count estimates with an error of 4% at best and 35% at worst for best orientation. The results demonstrated the potential of our multiscale texture based approach to model data at scale on sediment topographic features linked to the activity of functionally important species. These methods in turn help to advance ecological research and estimate ecosystem services potential as well as provide an additional tool for large scale rapid ecological assessment and estuarine management.

Investigation of Chromatic Aberration and Its Influence on the Processing of Underwater Imagery

The number of researchers utilising imagery for the 3D reconstruction of underwater natural (e.g., reefs) and man-made structures (e.g., shipwrecks) is increasing. Often, the same procedures and software solutions are used for processing the images as in-air without considering additional aberrations that can be caused by the change of the medium from air to water. For instance, several publications mention the presence of chromatic aberration (CA). The aim of this paper is to investigate CA effects in low-cost camera systems (several GoPro cameras) operated in an underwater environment. We found that underwater and in-air distortion profiles differed by more than 1000 times in terms of maximum displacement and in terms of curvature. Moreover, significant CA effects were found in the underwater profiles that did not exist in-air. Furthermore, the paper investigates the effect of adjustment constraints imposed on the underwater self-calibration and the reliability of the interior orientation parameters. The analysis of the precision shows that in-air RMS values are just due to random errors. In contrast, the underwater calibration RMS values are 3x-6x higher than the exterior orientation parameter (EOP) precision, so these values contain both random error and the systematic effects from the CA. The accuracy assessment shows significant differences.

A COMPARISON OF LOW-COST CAMERAS APPLIED TO FIXED MULTI-IMAGE MONITORING SYSTEMS

Abstract. Photogrammetry is becoming a widely used technique for slope monitoring and rock fall data collection. Its scalability, simplicity of components and low costs for hardware and operations makes its use constantly increasing for both civil and mining applications. Recent on site permanent installation of cameras resulted particularly viable for the monitoring of extended surfaces at very reasonable costs. The current work investigates the performances of a customised Raspberry Pi camera module V2 system and three additional low-cost camera systems including an ELP-USB8MP02G camera module, a compact digital camera (Nikon S3100) and a DSLR (Nikon D3). All system, except the Nikon D3, are available at comparable price. The comparison was conducted by collecting images of rock surfaces, one located in Australia and three located in Italy, from distances between 55 and 110 m. Results are presented in terms of image quality and three dimensional reconstruction error. Thereby, the multi-view reconstructions are compared to a reference model acquired with a terrestrial laser scanner.

Traffic sign detection and positioning based on monocular camera

ABSTRACTGeospatial and geometric states of traffic signs are indispensable information for traffic facility maintenance. This study applies a low-cost single camera system to locate and identify traffic signs and reflects their on-site conditions for assisting in maintenance. Referring to official regulations, a traffic sign can be identified and classified based on its color and shape attributes. The poses and states of the sign planes can also be evaluated according to their geometric shape ratios and the discrepancy with respect to traffic sign regulations, in which a least-square consistency check is proposed to ensure assessment reliability and accuracy. Validation with day and nighttime image sequences was performed to verify the effectiveness of the proposed method and the robustness to illumination changes, color deterioration, and motion blur. Considering the promising results, this study can be deemed as an alternative to promote routine maintenance of traffic facilities.

Time lapse photography with two different camera systems for in situ observation of the bivalve Diplodon chilensis (Gray, 1828) in a southern Chilean lake

During the last 40 years time lapse photography has become a strong tool for in situ observations of marine and freshwater wildlife. We compared the validity of two low cost camera systems to analyze the filtration activity of Diplodon chilensis (Gray, 1828), the most common freshwater bivalve in southern Chile. One camera system (Nikon D300) was connected with an external flash, whereas the other system (GoPro Hero 3+) contained a permanent video light source. Size change of the total area of the exhalant siphon (ESA) of Diplodon individuals was used to track activity changes over time. Each ESA is expressed as the percentage of the maximum siphon opening of the corresponding specimen. The siphon of D. chilensis was completely closed for 3.4% ± 5.2% (mean ± SD) of the recording time and it shows a nearly completely opened siphon for 42.7% ± 22.9% of the recording time. The mean siphon opening was 68.3% ± 26.5% of its potential opening area. Although a red filter (permeable to wavelengths above 600 nm ) was placed in front of the video light source, the mobile fauna, especially Aegla abtao (Schmitt, 1942) and unidentified small fishes were highly attracted by the permanent light, whereas this effect was not visible when a flash light in intervals of 2 min was used. We conclude that both low cost cameras are very suitable for in situ time lapse observation of freshwater clams.

Automated Sensing of Wave Inundation across a Rocky Shore Platform Using a Low-Cost Camera System

Rocky coastlines are frequently used for recreation, however, they are often highly exposed and hazardous environments resulting in high risk to visitors. Traditional approaches to managing human safety in coastal settings (such as the surf lifesaving clubs that have proven effective on beaches) are not necessarily transferable to rock platforms due to their often remote and fragmented distribution and the different recreational uses. As such, a different approach is required. To address this, we present a low-cost camera system to assess the wave hazard on a high-visitation rocky shore platform: the Figure Eight Pools Rock Platform, New South Wales, Australia. The camera system is shown to be highly effective and allows identification of both the distance and frequency of wave inundation on the platform using a novel pixel analysis technique. Nearshore wave height is shown to be the primary factor driving inundation frequencies along the cross-platform transect investigated with some influence from wave period. The remotely sensed camera data are used to develop a preliminary overwash hazard rating system, and analysis of the first month of data collected suggests that the platform is highly hazardous to visitors. Future work will expand this hazard rating system, developing a predictive tool that estimates the overwash hazard level based on forecast wave and tide conditions to improve visitor safety at the site.

ACCURACY ASSESSMENT OF UNDERWATER PHOTOGRAMMETRIC THREE DIMENSIONAL MODELLING FOR CORAL REEFS

Abstract. Recent advances in automation of photogrammetric 3D modelling software packages have stimulated interest in reconstructing highly accurate 3D object geometry in unconventional environments such as underwater utilizing simple and low-cost camera systems. The accuracy of underwater 3D modelling is affected by more parameters than in single media cases. This study is part of a larger project on 3D measurements of temporal change of coral cover in tropical waters. It compares the accuracies of 3D point clouds generated by using images acquired from a system camera mounted in an underwater housing and the popular GoPro cameras respectively. A precisely measured calibration frame was placed in the target scene in order to provide accurate control information and also quantify the errors of the modelling procedure. In addition, several objects (cinder blocks) with various shapes were arranged in the air and underwater and 3D point clouds were generated by automated image matching. These were further used to examine the relative accuracy of the point cloud generation by comparing the point clouds of the individual objects with the objects measured by the system camera in air (the best possible values). Given a working distance of about 1.5 m, the GoPro camera can achieve a relative accuracy of 1.3 mm in air and 2.0 mm in water. The system camera achieved an accuracy of 1.8 mm in water, which meets our requirements for coral measurement in this system.

ACCURACY ASSESSMENT OF UNDERWATER PHOTOGRAMMETRIC THREE DIMENSIONAL MODELLING FOR CORAL REEFS

Recent advances in automation of photogrammetric 3D modelling software packages have stimulated interest in reconstructing highly accurate 3D object geometry in unconventional environments such as underwater utilizing simple and low-cost camera systems. The accuracy of underwater 3D modelling is affected by more parameters than in single media cases. This study is part of a larger project on 3D measurements of temporal change of coral cover in tropical waters. It compares the accuracies of 3D point clouds generated by using images acquired from a system camera mounted in an underwater housing and the popular GoPro cameras respectively. A precisely measured calibration frame was placed in the target scene in order to provide accurate control information and also quantify the errors of the modelling procedure. In addition, several objects (cinder blocks) with various shapes were arranged in the air and underwater and 3D point clouds were generated by automated image matching. These were further used to examine the relative accuracy of the point cloud generation by comparing the point clouds of the individual objects with the objects measured by the system camera in air (the best possible values). Given a working distance of about 1.5 m, the GoPro camera can achieve a relative accuracy of 1.3 mm in air and 2.0 mm in water. The system camera achieved an accuracy of 1.8 mm in water, which meets our requirements for coral measurement in this system.

Development of Near-Diffraction Limited, Low-Cost and High-Resolution CCD Camera System for Imaging Coulter Orifices

A low-cost CCD camera system for imaging Coulter orifices ranging in diameter between 20 µm and 2 mm has been developed and tested. The imaging system incorporates a 6X magnifying lens for viewing the required range of Coulter orifices and LEDs (Light emitting diodes) lamp for back illuminating the orifices. Geometric optic calculations using Zemax® for the micro-lens interfaced with the camera suggest that the spot diameter and the MTF spatial frequency at field of view equal to 0 ° and at the image plane are 5.13 µm and 271.6 lines/mm (at contrast= 37.6%), respectively. Images captured with the camera system for 20 µm, 100 µm and 2 mm diameter orifices are provided. Furthermore, a discussion for the camera micro-lens modulation transfer function, spot diagram, root-mean-square wavefront error versus field and optical path difference plots is given.

LOW-COST OPTICAL CAMERA SYSTEM FOR DISASTER MONITORING

Abstract. Real-time monitoring of natural disasters, mass events, and large accidents with airborne optical sensors is an ongoing topic in research and development. Airborne monitoring is used as a complemental data source with the advantage of flexible data acquisition and higher spatial resolution compared to optical satellite data. In cases of disasters or mass events, optical high resolution image data received directly after acquisition are highly welcomed by security related organizations like police and rescue forces. Low-cost optical camera systems are suitable for real-time applications as the accuracy requirements can be lowered in return for faster processing times. In this paper, the performance of low-cost camera systems for real-time mapping applications is exemplarily evaluated based on already existing sensor systems operated at German Aerospace Center (DLR). Focus lies next to the geometrical and radiometric performance on the real time processing chain which includes image processors, thematic processors for automatic traffic extraction and automatic person tracking, data downlink to the ground station, and further processing and distribution on the ground. Finally, a concept for a national airborne rapid mapping service based on the low-cost hardware is proposed.

Low-cost Camera System for Online Estimation of Grain Size in Fluidized Bed

AbstractAt present, the production of energy has to be capable of responding to fast and unexpected changes, which may originate from changes in the fuel quality or in the energy demands, for example. New fuels and their co-combustion, added to the growing demands for process efficiency and the efforts to reduce harmful emissions have generated an increasing need for monitoring the processes, and new kinds of measurements and tools are needed to meet those challenges. Monitoring the condition of the bed is increasingly important in fluidized bed combustion of biomass, and it is usually performed manually by sieving the bottom ash. This is expensive and laborious, however, and should be either automated or alternative methods for estimating the bed quality should be developed if possible. In this paper we present an online inspection system for estimating the coarseness of the bed material using digital images taken from the bottom ash.

See-and-avoid comparison of performance in manned and remotely piloted aircraft

See-and-avoid is the current FAA approved method for pilots to avoid objects and other aircraft while flying in visual meteorological conditions (VMC). Although fully autonomous “sense-and-avoid” or “detect-and-avoid” systems are in development, none are currently certified. Thus existing unmanned aerial vehicle (UAV) operations are limited to case-by-case restricted airspace or require escort by manned aircraft. Many UAVs are equipped with at least a forward-looking camera. In the transition between current technology and future fully autonomous, certified sense-and-avoid systems, it seems reasonable to require a ground-based operator to perform the see-and-avoid function. This discusses the flight-testing performed to establish air traffic detection ranges for low-time pilots, and for a low-cost UAV camera system. The system was evaluated to determine if it could provide the equivalent see-and-avoid performance as the tested pilots.