Simple Camera Research Articles

Suitable Methods of Inoculation and Quantification of Fusarium Root Rot in Lentil.

Lentil (Lens culinaris L. subsp. culinaris) is an important grain legume grown worldwide. As its popularity grows among consumers and more acres are produced, new root rot complexes have become more prevalent. This work sought to develop methods for studying root rot caused by Fusarium avenaceum in lentil using controlled environments. The objectives were to (i) find an effective and seed-safe sterilization technique, (ii) optimize the inoculation technique and lentil growing environment, and (iii) develop visual and automated disease scoring systems. Results showed the use of detergent and a low concentration (0.1%) of NaClO (the active ingredient in bleach) maintained germinability and effectively eliminated bacterial and fungal contamination on seeds. Other treatments, such as ethanol, reduced seed germination or failed to kill pathogenic fungi such as Fusarium spp. Placing inoculum at a moderate rate of 1 × 106 spores both directly on the seed and on top of the media covering the seed improved severity scores and reduced escapes compared with placement on top of the media only. Visual severity scoring systems and diagrammatic scales were developed for scoring the cotyledon region and roots. A computer vision algorithm was designed to improve the efficiency of scoring the cotyledon region and roots for disease severity using a simple RGB camera and lightbox. Visual and computer scores were best correlated when images were visually scored on a monitor, and multiple images were averaged. The scores generated from the computer vision algorithm had better correlations with visual scores for cotyledon rot (r = 0.92 and β1 = 0.96) than root rot (r = 0.62 and β1 = 0.67).

A simple trail camera modification reveals red-bellied woodpeckers as important egg predators of box-nesting wood ducks

Artificial nesting structures have been used to aid the recovery and maintenance of certain avian populations such as the wood duck (Aix sponsa). The success of nest boxes for wood ducks relies on excluding common nest predators such as raccoons (Procyon lotor) and rat snakes (Pantherophis spp.) via the use of predator guards; however, woodpeckers cannot be excluded. Red-bellied woodpeckers (Melanerpes carolinus) are foraging generalists and have been anecdotally observed preying on wood duck eggs where documenting predation events is difficult to accomplish through traditional nest box monitoring. Here, we used custom-modified trail cameras to document egg depredations and foraging events by red-bellied woodpeckers and the removal of damaged eggs by wood ducks during the laying and incubation periods. We captured 35,686 videos and documented 1173 different events consisting of 608 (51.8%) videos of red-bellied woodpeckers and 565 (48.2%) of wood ducks. We determined 38 (30.6%) eggs were depredated during the laying period and 14 (11.3%) during incubation. Wood ducks removed 23 (60.5%) eggs depredated during the laying period and 13 (92.9%) depredated during incubation. We found no difference in the daily number of times red-bellied woodpeckers visited boxes with nests in the laying or incubation period (t = −0.967, df = 14.699, P = 0.349). Red-bellied woodpeckers depredated 1.0 ± 0.2 eggs during each event and returned to forage on previously depredated eggs 5.3 ± 4.4 times each day spending 2.1 ± 1.1 min during each return. Our results suggest that red-bellied woodpeckers forage on eggs when nests are unguarded, and they have previously been under-recognized in the literature as predators of wood duck eggs. Future nest box programs should consider the impact red-bellied woodpeckers have on wood duck egg loss and nest survival.

Real-time unmanned aerial vehicle surveying using spatial criteria: a simulated study

Precise coordinate estimation is a fundamental engineering challenge in many mapping applications. Conventional surveying techniques based on global navigation satellite system (GNSS), light detection and ranging (LiDAR), or total stations involve costly equipment and time-consuming methodologies and may suffer from restrictions, such as occlusions and low satellite availability. In this study, a surveying approach, based on a custom-equipped unmanned aerial vehicle (UAV) and ArUco markers distributed in an unknown area as the potential target mapping points, is proposed and evaluated through simulation. The UAV incorporates a real-time kinematic GNSS receiver and a gimbal unit, with a simple camera and an electronic rangefinder module. The system demonstrates a real-time hierarchy targeting system that allows the UAV to engage with the ground targets through the camera, measure corresponding distances, record UAV coordinates, and then perform a multilateration-based target coordinate estimation. To evaluate the flexibility, efficiency, and onboard performance of the proposed target positioning approach, the method was developed as a robotic operating system software package and tested in the Gazebo robotic simulator on an NVIDIA Jetson TX2. Several mapping environments, along with varying flight type scenarios, were created to evaluate the resulting coordinate estimation errors. The achieved positioning accuracy is very promising and demonstrates a possible use for circular flying trajectories. Along these lines, the proposed methodology may pave the way toward a precise surveying alternative, as it may establish the main surveying method for common mapping applications in the near future and even provide coordinate estimations in demanding and, until now, unattainable areas.

Glow-in-the-Dark Infectious Disease Diagnostics Using CRISPR-Cas9-Based Split Luciferase Complementation.

Nucleic acid detection methods based on CRISPR and isothermal amplification techniques show great potential for point-of-care diagnostic applications. However, most current methods rely on fluorescent or lateral flow assay readout, requiring external excitation or postamplification reaction transfer. Here, we developed a bioluminescent nucleic acid sensor (LUNAS) platform in which target dsDNA is sequence-specifically detected by a pair of dCas9-based probes mediating split NanoLuc luciferase complementation. LUNAS is easily integrated with recombinase polymerase amplification (RPA), providing attomolar sensitivity in a rapid one-pot assay. A calibrator luciferase is included for a robust ratiometric readout, enabling real-time monitoring of the RPA reaction using a simple digital camera. We designed an RT-RPA-LUNAS assay that allows SARS-CoV-2 RNA detection without the need for cumbersome RNA isolation and demonstrated its diagnostic performance for COVID-19 patient nasopharyngeal swab samples. Detection of SARS-CoV-2 from samples with viral RNA loads of ∼200 cp/μL was achieved within ∼20 min, showing that RPA-LUNAS is attractive for point-of-care infectious disease testing.

Conceptual design of multichannel fast electron bremsstrahlung detection system to study fast electron dynamics during lower hybrid current drive in ADITYA-U tokamak

To drive plasma current non inductively, a Lower Hybrid Current Drive (LHCD) system with a passive-active multijunction (PAM) antenna for injecting lower hybrid wave (LHWs) has been designed, fabricated and to be integrated with ADITYA-U Tokamak. A fast electron population in the energy range of a few keV to several hundreds of keV is generated with the injection of the LHWs. These fast electrons interacting mainly with ions and electrons result in Hard X-Ray (HXR) emission called Fast Electron Bremsstrahlung acronym as FEB emission. A single channel FEB detection system is being used in the earlier experiment of LHCD in ADITYA-U tokamak. However, the single channel detection system can't provide a radial emissivity profile of HXR intensity distribution. In order to determine a radial emissivity profile, for the first time, a multichannel FEB detection system is designed for ADITYA-U tokamak. This paper describes the detailed conceptual design for the selection of suitable detectors, optimization of the collimators, shielding geometry, and low energy filtering cut-off. In the present design analysis, the soller collimator concept is considered over a simple pinhole camera system due to its several advantages. In order to optimize the multichannel FEB system parameters for the best possible performance, a forwarded modelling code has been used. The signal to background ratio at each detector location has been estimated for a few system parameters and reported herein.

Using deep learning for the prediction of mixing patterns in two component-colored solutions as a proxy to dispersion in nanocomposite coatings

Dispersion of nanofiller in the polymer matrix is a vital factor that influences the properties of the fabricated nanocomposites at the laboratory level. Characterization techniques like TEM and FESEM, having a small sample size, tend to miss out on the big picture for the analysis of mixing on a larger scale. At the industrial level, conducting such testing with varying experimental conditions is not viable in terms of both cost and time. Through this study, we propose a simple method to examine the extent of dispersion using a simple camera and employing deep learning (DL) models. For this purpose, an analogous study has been performed to study the sensitivity of the processing techniques, to better understand the findings in our previous articles. A two component-colored solution (oil–water) was utilized as a proxy for the nanofiller-polymer matrix system. Different processing methods were employed namely ultrasonication, homogenization, sequential ultrasonication and homogenization and simultaneous ultrasonication and homogenization. The variation in processing technique significantly affects the dispersion which is attributed to the different mixing mechanisms (turbulent, diffusive, and convective) incurred in these processing techniques. Inferences are withdrawn by detecting patterns in a large sample size which highlights that DL models provide us with a holistic viewpoint of real-time observations. It also ameliorates human interpretation by unraveling obscure information which can go unnoticed by human eyes.

Impact Evaluation of Score Classes and Annotation Regions in Deep Learning-Based Dairy Cow Body Condition Prediction.

Body condition scoring is a simple method to estimate the energy supply of dairy cattle. Our study aims to investigate the accuracy with which supervised machine learning, specifically a deep convolutional neural network (CNN), can be used to retrieve body condition score (BCS) classes estimated by an expert. We recorded images of animals' rumps in three large-scale farms using a simple action camera. The images were annotated with classes and three different-sized bounding boxes by an expert. A CNN pretrained model was fine-tuned on 12 and 3 BCS classes. Training in 12 classes with a 0 error range, the Cohen's kappa value yielded minimal agreement between the model predictions and ground truth. Allowing an error range of 0.25, we obtained minimum or weak agreement. With an error range of 0.5, we had strong or almost perfect agreement. The kappa values for the approach trained on three classes show that we can classify all animals into BCS categories with at least moderate agreement. Furthermore, CNNs trained on 3 BCS classes showed a remarkably higher proportion of strong agreement than those trained in 12 classes. The prediction precision when training with various annotation region sizes showed no meaningful differences. The weights of our trained CNNs are freely available, supporting similar works.

Non-contact based photogrammetric technique for testing of rigid blocky masonry arch

In case of dry jointed stone structural systems, the stone blocks can be assumed to be rigid with relative predominant displacements occurring along the joints giving rise to geometric non-linearity in these systems. The testing of such discontinuous systems becomes challenging task with contact-based measuring instruments such as LVDTs as the blocks are free to displace and/or rotate with six degrees of freedom. This technique is more often used for surveying of large existing structures in the field of civil engineering and its applicability to testing of structural systems has not been explored to a larger extent yet. The photogrammetry with motion module has been observed to be the one of the most reliable and feasible methods of instrumentation for dry jointed stone structural systems. With few quality control measures adopted from taking photos to the processing stage, sufficient accuracy of measurements can be achieved. In the present study, non-contact-based technique, photogrammetry is used for testing of a typical dry-stone corbel vault taking a scaled down model of an existing vaulted gallery of Ta Prohm, Cambodia. The findings from the study have been in agreement with the observed distress in the existing corbel vaulted galleries. It has been proved to be efficient, low cost method that uses simple handheld digital cameras giving reliable results with an accuracy up to 0.4 millimetres. Thus, photogrammetry is a way forward for testing of discontinuous structural systems such as dry-stone masonry.

Mosaic-free compound eye camera based on multidirectional photodetectors and single-pixel imaging.

Compound-eye wide field-of-view (FOV) imaging generally faces the disadvantages of a complex system, low resolution, and complicated image mosaic. Single-pixel imaging has proven to very beneficial in building a high-resolution and simple wide-FOV camera, but its ability to overcome the problem of image mosaics still needs to be demonstrated. In this Letter, we propose a novel, to the best of our knowledge, kind of artificial compound eye based on multidirectional photodetectors (PDs) and demonstrate theoretically and experimentally that mosaics are unnecessary in multidirectional PD-based single-pixel imaging. In addition, we show experimentally that only nine multidirectional PDs are needed to obtain wide-angle images in a hemisphere to realize wide-FOV mosaic-free imaging. This work greatly simplifies the concept of compound-eye cameras and is very enlightening for detector design in wide-FOV single-pixel imaging, plausibly leading to the development of single-pixel endoscopic imaging.

C.DOT - Convolutional Deep Object Tracker for Augmented Reality Based Purely on Synthetic Data.

Augmented reality applications use object tracking to estimate the pose of a camera and to superimpose virtual content onto the observed object. Today, a number of tracking systems are available, ready to be used in industrial applications. However, such systems are hard to handle for a service maintenance engineer, due to obscure configuration procedures. In this article, we investigate options towards replacing the manual configuration process with a machine learning approach based on automatically synthesized data. We present an automated process of creating object tracker facilities exclusively from synthetic data. The data is highly enhanced to train a convolutional neural network, while still being able to receive reliable and robust results during real world applications only from simple RGB cameras. Comparison against related work using the LINEMOD dataset showed that we are able to outperform similar approaches. For our intended industrial applications with high accuracy demands, its performance is still lower than common object tracking methods with manual configuration. Yet, it can greatly support those as an add-on during initialization, due to its higher reliability.

Utilizing Mask R-CNN for Solid-Volume Food Instance Segmentation and Calorie Estimation

To prevent or deal with chronic diseases, using a smart device, automatically classifying food categories, estimating food volume and nutrients, and recording dietary intake are considered challenges. In this work, a novel real-time vision-based method for solid-volume food instance segmentation and calorie estimation is utilized, based on Mask R-CNN. In order to address the proposed method in real life, distinguishing it from other methods which use 3D LiDARs or RGB-D cameras, this work applies RGB images to train the model and uses a simple monocular camera to test the result. Gimbap is selected as an example of solid-volume food to show the utilization of the proposed method. Firstly, in order to improve detection accuracy, the proposed labeling approach for the Gimbap image datasets is introduced, based on the posture of Gimbap in plates. Secondly, an optimized model to detect Gimbap is created by fine-tuning Mask R-CNN architecture. After training, the model reaches AP (0.5 IoU) of 88.13% for Gimbap1 and AP (0.5 IoU) of 82.72% for Gimbap2. mAP (0.5 IoU) of 85.43% is achieved. Thirdly, a novel calorie estimation approach is proposed, combining the calibration result and the Gimbap instance segmentation result. In the fourth section, it is also shown how to extend the calorie estimation approach to be used in any solid-volume food, such as pizza, cake, burger, fried shrimp, oranges, and donuts. Compared with other food calorie estimation methods based on Faster R-CNN, the proposed method uses mask information and considers unseen food. Therefore, the method in this paper outperforms the accuracy of food segmentation and calorie estimation. The effectiveness of the proposed approaches is proven.

Remote AFIS: development and validation of low-cost remote tower concepts for uncontrolled aerodromes

Remote tower systems are widely established as a means to provide efficient air traffic control (ATC) from a remote location. However, even these cost-effective systems cause procurement, implementation, and maintenance costs, which make them unaffordable for non-ATC aerodromes with low revenues, often only offering an aerodrome flight information service (AFIS) or UNICOM information service. In this paper, two more inexpensive concepts enabling remote control at these aerodromes are presented. They are based on a simplified camera set-up comprising a pan-tilt-zoom-camera (PTZ-camera) and a simple panoramic camera. A virtual reality-headset (VR-headset) is used to display the video streams and to control the PTZ-camera. The results of a validation study with nine ATC and AFIS officers using live traffic at the Braunschweig-Wolfsburg aerodrome (BWE) are presented. They are discussed with respect to perceived usability, virtual reality induced cybersickness, and operator acceptance. The system’s cost is compared to that of a conventional remote tower set-up. Furthermore, measured objective data in the form of angular head rotation velocities are presented and requirements for the camera set-up are deduced. In conclusion, the developed concept utilizing both the panorama camera and PTZ-camera was found to be sufficiently usable and accepted by the validation participants. In contrast, the concept based only on the PTZ-camera suffered from jerky and delayed camera movements leading to considerable cybersickness and making it barely usable.

Novel Optimization Approach for Camera Calibration in Traffic Scenes

Currently, most camera calibration methods for traffic scenes are based on vanishing points and road geometry markings with simplified camera models, which can only be applied to scenes containing straight roads. However, in practical applications, cameras are usually installed with roll angles and scenes containing curved roads, to which existing methods are not applicable. To solve the above problems, we propose a novel optimization approach for camera calibration in traffic scenes, which can be applied to curved road scenes and predict camera roll angle. Firstly, a camera space model with a camera roll angle is established for image rotation. Secondly, vehicle trajectories are extracted for the best vanishing point by a parallel coordinate system and diamond space. Vehicle trajectories are also used to obtain calibration regions for extracting road markings and edges. The road markings, edges, and the best vanishing point obtained by the above two steps automatically are more accurate and stable, especially for curved road scenes. Based on the road markings and the best vanishing point, initial calibration can be conducted. Finally, by extracting redundant markings in the calibration region, the non-linear constraint of redundant markings on the road is proposed to obtain optimized calibration parameters and predict the camera roll angle. Through experimental validation on the public dataset BrnoCompSpeed and highway scenes, the proposed approach can achieve better calibration results in both straight and curved road scenes with the mean calibration error reduced by 30% compared with the previous calibration methods.

Design of pediatric robot to simulate infant biomechanics for neuro-developmental assessment in a sensorized gym.

Infants at risk for developmental delays often exhibit postures and movements that may provide a window into potential impairment for cerebral palsy and other neuromotor conditions. We developed a simple 4 DOF robot pediatric simulator to help provide insight into how infant kinematic movements may affect the center of pressure (COP), a common measure thought to be sensitive to neuromotor delay when assessed from supine infants at play. We conducted two experiments: 1) we compared changes in COP caused by limb movements to a human infant and 2) we determined if we could predict COP position due to limb movements using simulator kinematic pose retrieved from video and a sensorized mat. Our results indicate that the limb movements alone were not sufficient to mimic the COP in a human infant. In addition, we show that given a robot simulator and a simple camera, we can predict COP measured by a force sensing mat. Future directions suggest a more complex robot is needed such as one that may include trunk DOF.

Improving Office Workers’ Workspace Using a Self-adjusting Computer Screen

With the rapid evolution of technology, computers and their users’ workspaces have become an essential part of our life in general. Today, many people use computers both for work and for personal needs, spending long hours sitting at a desk in front of a computer screen, changing their pose slightly from time to time. This phenomenon impacts people’s health negatively, adversely affecting their musculoskeletal and ocular systems. To mitigate these risks, several different ergonomic solutions have been suggested. This study proposes, demonstrates, and evaluates a technological solution that automatically adjusts the computer screen position and orientation to its user’s current pose, using a simple RGB camera and robotic arm. The automatic adjustment will reduce the physical load on users and better fit their changing poses. The user’s pose is extracted from images continuously acquired by the system’s camera. The most suitable screen position is calculated according to the user’s pose and ergonomic guidelines. Thereafter, the robotic arm adjusts the screen accordingly. The evaluation was done through a user study with 35 users who rated both the idea and the prototype system itself highly.

In situ quality monitoring in direct energy deposition process using co-axial process zone imaging and deep contrastive learning

Many strategic industrial sectors prefer Directed Energy Deposition (DED) to other Additive Manufacturing (AM) technologies due to the high material deposition and build rates. However, the inadvertent formation of defects such as porosity, micro-cracks and microstructure anomalies hinders its adoption in industries that require specific mechanical and microstructural properties. These defects are caused by undesirable fluctuations in process conditions such as material flow rate, laser power, melt pool dynamics, environment gas composition, temperature gradients. This research proposes in situ quality monitoring of DED using images of process zone and contrastive learning-based Convolutional Neural Network (CNN). Experiments included deposition of titanium powder (Cp-Ti, grade 1) with the particle size ranging between 45 and 106 μm on the base plate (99.6 % Ti6Al4V grade 1), forming a cube geometry. The process parameters were tuned to achieve six quality grades. The video of the process zone was recorded co-axially to the laser beam during the entire manufacturing, which was eventually used as the input to train CNN's based on contrastive losses. An in situ monitoring strategy for classifying the different quality grades was demonstrated in a supervised and semi-supervised manner, with an accuracy ranging between 89 % and 97 %. The performance of the developed framework was compared to an alternative clustering technique, namely t-distributed stochastic neighbour embedding, justifying the efficiency of our approach. The developed methodology demonstrates the possibility to track workpiece manufacturing quality using simple CCD cameras with minimum interventions on the commercial machines.

On-Street Parking Occupancy Inference Based on Payment Transactions

A record of spatial and temporal parking occupancy is critical to optimize on-street parking resources and to develop effective parking policies. Such data are often obtained through advanced and costly occupancy monitoring technologies. Moreover, it is usually challenging to integrate bay-level occupancies and parking data from other systems. Accurate occupancy–payment data are required for a wide range of analytical and practical purposes, including but not limited to investigating payment behavior, estimating and forecasting occupancy, and evaluating the efficiency and effectiveness of enforcement policies. This study proposes a metaheuristic optimization algorithm to integrate snapshots of bay-level parking occupancy, captured using simple cameras, with transactions from a conventional parking payment management system. The resulting integrated data were used to develop, calibrate and validate a parking occupancy estimation method utilizing parking payment data only. Details of the design, implementation, and validation of the proposed algorithm and modelling technique are provided. Logistic regression analysis was used to tune parameters of the data integration algorithm. Deep learning, gradient boosting and random forests were used to develop a model of parking occupancy. Evaluation of the algorithm indicated an accuracy of 76% of correct data integration; that is, individual bay occupancies integrated with the correct corresponding payment transactions. The best occupancy estimation model also showed a very high accuracy, with an R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> above 94% and a root mean square error (RMSE) of 1.2 (occupied bays), when tested with a random sample from the integrated data.

Diving Lessons

Diving Lessons

The Vision-Based Data Reader in IoT System for Smart Factory

The proposed research is based on a real plastic injection factory for cutting board production. Most existing approaches for smart manufacturing tried to build the total solution of IoT by moving forward to the standard of industry 4.0. Under the cost considerations, this will not be acceptable to most factories, so we proposed the vision based technology to solve their immediate problem. Real-time machine condition monitoring is important for making great products and measuring line productivity or factory productivity. The study focused on a vision-based data reader (VDR) in edge computing for smart factories. A simple camera embedded in Field Programmable Gate Array (FPGA) was attached to monitor the screen on the control panel of the machines. Each end device was preprogrammed to capture images and process data on its own. The preprocessing step was then performed to have the normalized illumination of the captured image. A saliency map was generated to detect the required region for recognition. Finally, digit recognition was performed and the recognized digits were sent to the IoT system. The most significant contribution of the proposed VDR system used the compact deep learning model for training and testing purposes to fit the requirement of cost consideration and real-time monitoring in edge computing. To build the compact model, different convolution filters were tested to fit the performance requirement. Experimentations on a real plastic cutting board factory showed the improvement in manufacturing products by the proposed system and achieved a high digit recognition accuracy of 97.56%. In addition, the prototype system had low power and low latency advantages.

High-Accuracy 3D Gaze Estimation with Efficient Recalibration for Head-Mounted Gaze Tracking Systems.

The problem of 3D gaze estimation can be viewed as inferring the visual axes from eye images. It remains a challenge especially for the head-mounted gaze tracker (HMGT) with a simple camera setup due to the complexity of the human visual system. Although the mainstream regression-based methods could establish the mapping relationship between eye image features and the gaze point to calculate the visual axes, it may lead to inadequate fitting performance and appreciable extrapolation errors. Moreover, regression-based methods suffer from a degraded user experience because of the increased burden in recalibration procedures when slippage occurs between HMGT and head. To address these issues, a high-accuracy 3D gaze estimation method along with an efficient recalibration approach is proposed with head pose tracking in this paper. The two key parameters, eyeball center and camera optical center, are estimated in head frame with geometry-based method, so that a mapping relationship between two direction features is proposed to calculate the direction of the visual axis. As the direction features are formulated with the accurately estimated parameters, the complexity of mapping relationship could be reduced and a better fitting performance can be achieved. To prevent the noticeable extrapolation errors, direction features with uniform angular intervals for fitting the mapping are retrieved over human’s field of view. Additionally, an efficient single-point recalibration method is proposed with an updated eyeball coordinate system, which reduces the burden of calibration procedures significantly. Our experiment results show that the calibration and recalibration methods could improve the gaze estimation accuracy by 35 percent (from a mean error of 2.00 degrees to 1.31 degrees) and 30 percent (from a mean error of 2.00 degrees to 1.41 degrees), respectively, compared with the state-of-the-art methods.