Low-cost Web Camera Research Articles

Design of a laboratory scale Automatic Optical Inspection prototype system for scanning of conveyor belt surfaces — A case study

The design and assembly of an Automatic Optical Inspection (AOI) system is a complex process for any production scenario that considers process quality outcome and loss as an important aspect. This paper discusses the design and implementation of a laboratory-based AOI system through inspection parameter optimization for monitoring the surface condition of conveyor belt segments. The system utilizes a low-cost web camera and a line laser projection device as sensor components in a non-intrusive setup to capture color and depth images of the belt surface. A mixed-level full factorial design of experiment (DoE) approach is devised to examine how different control factors, which in this case are the inspection parameters, such as- the surface illumination, inspection speed, imaging distance, camera frame rate, and camera field of view, affect the response factor, which is the measurement error percentage of the AOI system. The error percentage is obtained by comparing the inspection results of a belt region of known dimensions with corresponding manual measurements. Comprehensive data collection runs followed by the factorial analysis revealed that all individual inspection parameters were significant except for the interaction between imaging distance and field of view. Based on the range of inspection parameter settings investigated, the optimal settings were found to be an imaging distance of 60 cm, a field of view of 75, illumination of 0 lux, an inspection speed of 9 cm/s, and a camera frame rate of 75 frames per second to achieve an inspection error of 0.26%, which is acceptable for our investigation.

Design of a Low-Cost Diffuse Optical Mammography System for Biomedical Image Processing in Breast Cancer Diagnosis.

Worldwide, breast cancer is the most common type of cancer that mainly affects women. Several diagnosis techniques based on optical instrumentation and image analysis have been developed, and these are commonly used in conjunction with conventional diagnostic devices such as mammographs, ultrasound, and magnetic resonance imaging of the breast. The cost of using these instruments is increasing, and developing countries, whose deaths indices due to breast cancer are high, cannot access conventional diagnostic methods and have even less access to newer techniques. Other studies, based on the analysis of images acquired by traditional methods, require high resolutions and knowledge of the origin of the captures in order to avoid errors. For this reason, the design of a low-cost diffuse optical mammography system for biomedical image processing in breast cancer diagnosis is presented. The system combines the acquisition of breast tissue photographs, diffuse optical reflectance (as a biophotonics technique), and the processing of digital images for the study and diagnosis of breast cancer. The system was developed in the form of a medical examination table with a 638 nm red-light source, using light-emitted diode technology (LED) and a low-cost web camera for the acquisition of breast tissue images. The system is automatic, and its control, through a graphical user interface (GUI), saves costs and allows for the subsequent analysis of images using a digital image-processing algorithm. The results obtained allow for the possibility of planning in vivo measurements. In addition, the acquisition of images every 30° around the breast tissue could be used in future research in order to perform a three-dimensional (3D) reconstruction and an analysis of the captures through deep learning techniques. These could be combined with virtual, augmented, or mixed reality environments to predict the position of tumors, increase the likelihood of a correct medical diagnosis, and develop a training system for specialists. Furthermore, the system allows for the possibility to develop analysis of optical characterization for new phantom studies in breast cancer diagnosis through bioimaging techniques.

Compact and low-cost deep-ultraviolet microscope system for label-free molecular imaging and point-of-care hematological analysis.

Deep-ultraviolet (UV) microscopy enables label-free, high-resolution, quantitative molecular imaging and enables unique applications in biomedicine, including the potential for fast hematological analysis at the point-of-care. UV microscopy has been shown to quantify hemoglobin content and white blood cells (five-part differential), providing a simple alternative to the current gold standard, the hematological analyzer. Previously, however, the UV system comprised a bulky broadband laser-driven plasma light source along with a large and expensive camera and 3D translation stage. Here, we present a modified deep-UV microscope system with a compact footprint and low-cost components. We detail the novel design with simple, inexpensive optics and hardware to enable fast and accurate automated imaging. We characterize the system, including a modified low-cost web-camera and custom automated 3D translation stage, and demonstrate its ability to scan and capture large area images. We further demonstrate the capability of the system by imaging and analyzing blood smears, using previously trained networks for automatic segmentation, classification (including 5-part white blood cell differential), and colorization. The developed system is approximately 10 times less expensive than previous configurations and can serve as a point-of-care hematology analyzer, as well as be applied broadly in biomedicine as a simple compact, low-cost, quantitative molecular imaging system.

Affine Layer-Enabled Transfer Learning for Eye Tracking with Facial Feature Detection in Human–Machine Interactions

Eye tracking is an important technique for realizing safe and efficient human–machine interaction. This study proposes a facial-based eye tracking system that only relies on a non-intrusive, low-cost web camera by leveraging a data-driven approach. To address the challenge of rapid deployment to a new scenario and reduce the workload of the data collection, this study proposes an efficient transfer learning approach that includes a novel affine layer to bridge the gap between the source domain and the target domain to improve the transfer learning performance. Furthermore, a calibration technique is also introduced in this study for model performance optimization. To verify the proposed approach, a series of comparative experiments are conducted on a designed experimental platform to evaluate the effects of various transfer learning strategies, the proposed affine layer module, and the calibration technique. The experiment results showed that the proposed affine layer can improve the model’s performance by 7% (without calibration) and 4% (with calibration), and the proposed approach can achieve state-of-the-art performance when compared to the others.

Real-Time Motion Analysis System Using Low-Cost Web Cameras and Wearable Skin Markers.

COVID-19 has restricted outdoor exercise and hospital visits for rehabilitation therapy. Home-based training and rehabilitation coaching systems have emerged as a way to overcome these circumstances. Conventional optical motion-capture systems, such as VICON, have been used for measuring precise movement and providing posture feedback during exercise or rehabilitation; however, its application is limited to professional facilities because of its high cost and space requirement. To extend the applicability to home-based use, we designed wearable skin markers (WSMs) with body segment-specific patterns that can be detected by low-cost web cameras. WSMs are band-shaped and stretchable and thus can be worn like cloth, with minimal effort for placement. The body segment-specific patterns enable real-time data processing, which reduces the marker data post-processing time. A 6-degree-of-freedom (DOF) pose for each WSM is obtained by recognizing the segment-specific patterns; the 3D configuration of the contoured corners of the patterns found by triangulation is then utilized to construct the coordinates of each WSM. The WSM system was validated via three experiments. The robustness of marker recognition was evaluated by measuring the false-positive and false-negative rates of WSM. For accuracy validation, the angle estimation results were obtained for the mechanical joint of a 3-DOF gimbal and lower-limb joints of a walking human subject and compared to the reference systems. The gimbal experiment was included to evaluate the accuracy of our system in the condition with no skin movement artifact. The maximum standard deviation of the difference between WSM and the encoder was 0.9 for the gimbal experiment, and that between WSM and VICON was 5.0 for the human experiment. The accuracy was comparable to the reference systems, making it suitable for home environment application.

A Deep Learning-Based Approach to Video-Based Eye Tracking for Human Psychophysics.

Real-time gaze tracking provides crucial input to psychophysics studies and neuromarketing applications. Many of the modern eye-tracking solutions are expensive mainly due to the high-end processing hardware specialized for processing infrared-camera pictures. Here, we introduce a deep learning-based approach which uses the video frames of low-cost web cameras. Using DeepLabCut (DLC), an open-source toolbox for extracting points of interest from videos, we obtained facial landmarks critical to gaze location and estimated the point of gaze on a computer screen via a shallow neural network. Tested for three extreme poses, this architecture reached a median error of about one degree of visual angle. Our results contribute to the growing field of deep-learning approaches to eye-tracking, laying the foundation for further investigation by researchers in psychophysics or neuromarketing.

An image processing-based crack detection technique for pressed panel products

Crack detection is an important step in assessing the quality of pressed panel products. This paper presents a fast and non-invasive crack detection technique which involves extracting the outline of the captured object and applying a unique edge line evaluation. This technique is robust against environmental condition changes and only require a low-cost web camera. After capturing an image immediately following the press process, a clear one-pixel edge line is extracted by applying a light control and a series of pre-image processing algorithms, including a valley-emphasis Otsu method and percolation-based shape recognition. Next, the initial detection at low resolution is applied to search for every possible crack using unique edge line and curvature evaluation. Finally, at high resolution, the windowed image of every possible crack is individually analyzed to detect existing cracks using a more specific evaluation process. All of these steps are completed within 0.5 s, thus allowing for the technique to be applied in real-time on a highly automated manufacturing line. To demonstrate the performance of the proposed technique, experiments are conducted on an aluminum plate with different patterns and the pressed panel products. The results show that the proposed technique can detect surface cracks on pressed panels with stable performance as well as high accuracy and efficiency.

Automated Asian Fruit Grading System Using Stereo Vision Technique

In Malaysia, most of fruit planters are still using the conventional methods of inspection and grading their products by manual weightage measurement and visual inspection. Conventional methods rely on human eyes to perform the grading which leads to unproductive system and lack of data management. Therefore, in this study, an automated weight grading system is proposed to perform the grading system via size measurement using stereo vision system and image processing. The fruit selected to verify the system proposed performance were fruit samples provided by MARDI Bukit Tangga located in Kedah, Malaysia. In the vision system, triangulation method is used where the samples were graded more accurately using two cameras to form a stereo vision system. The image capture through the vision system processed using multiple image processing steps such as image segmentation, sobel edge sensor and multiple morphological operation. The relationship between weight and area of fruits done via regression methods with adjusted R-square of 0.9823. Using this goodness of fit, weight estimation can be executed with Root Mean Squared Error (RMSE) of 6.811. Overall, fruit grading system with the stereo vision of a low-cost web camera implementation has successfully performed with the accuracy of 85.65% and a Mean Absolute Percentage Error (MAPE) of 6.551%.

Automatic Pupillary Light Reflex Detection in Eyewear Computing

There are many benefits to facilitating “always-on” pupillary light reflex (PLR)-aware pupil size measurement in eyewear, including improving the reliability of pupil-based cognitive and affective load monitoring and enabling PLR-based diagnosis of cognitive and eye-related diseases which have neurological symptoms manifested in the form of aberrant PLR responses. However, the detection of PLR responses for application in eyewear devices for everyday usage, beyond PLR measurement in confined clinical sessions, has not been investigated. To this end, a means of characterizing PLRs in less controlled environmental settings is investigated and subsequently a method of PLR detection is developed and evaluated. A low-cost head-mounted Web camera was used to record near-field eye video sequences which were processed with the self-tuning threshold algorithm for pupil diameter estimation and blink detection. PLR was induced by luminance change of a monitor and brightness change of the displayed image on a monitor. A transient model-based PLR detection algorithm which utilizes the general correlation of PLR amplitude and velocity was developed and evaluated on the data sets in terms of false alarm and false rejection rates. The findings from this research suggest that the PLR can be detected reliably using low-cost wearable pupil-measurement systems without using a separate sensor for detecting the luminance conditions. The correlation between pupil diameter amplitude and maximum velocity of PLR was shown to be sufficiently consistent for PLR detection.

Stereovision system for estimating tractors and agricultural machines transit area under orchards canopy

Managing orchards requires delicate agricultural operations being typically carried out in narrow zones where the operators usually drive machineries under stress that could result in poor performance. In such conditions, the use of technology would help manage the machines to reduce the hazardous work and eventual damage to the plants. To safely drive a tractor, the driver needs to be aware of its surroundings, thus a stereovision system can provide helpful information. Stereo imaging has proven to be an effective three-dimensional vision system. Indeed, the range (or third coordinate) information is useful to detect the obstacle distances. Such distances, when detected during agricultural operations, could be used to assist the operator in driving the tractor at regular or variable working speeds and eventually to provide manufacturers useful indications to model the form of ROPS (roll over protection structure). This study aimed to verify the closeness of agreement between manual and stereo-image measurements, and thus to provide helpful information regarding safety and working purposes. The system used a custom low-cost dual web-camera in combination with an image analysis algorithm in order to automatically extract the information needed. Manual independent measurements were carried out using a metric tape (sensitivity 1 cm). A regular structure was used for the analysis: four rows of ten trees each one. Alternated red and blue paper markers were placed on the hazelnut trees (two per tree) of two couples of rows for enhanced visibility. For each couple of trees (one on the right, the other on the left), the four markers formed a trapezoid that was measured. The results of the analysis demonstrated that the stereo vision provided distance measurements with reasonable accuracy (error <5%) in the range of distances lower than 20 m. The resolution assessed for the developed video system is suitable for obtaining distance information in real scenes. This information could be used to assist drivers to operate agricultural machineries through narrow tree rows during work execution. Moreover, such information could be used for safeguarding decision-making and/or for controlling some tractor functions such as continuing moving, changing driving direction, changing 3-point hitch position, reducing transmission speed, halting the tractor. These functions will be necessary before tractors become fully autonomous. Finally, the measured distances, marking the narrow transitions between the tree rows, could be also used to study the ROPS form, both for working safely and for avoiding possible damage caused to the hazel trees laterally. Keywords: stereovision, precision agriculture, digital agriculture, hazelnut; canopy, ROPS, orchards DOI: 10.25165/j.ijabe.20191201.4123 Citation: Costa C, Febbi P, Pallottino F, Cecchini M, Figorilli S, Antonucci F, et al. Stereovision system for estimating tractors and agricultural machines transit area under orchards canopy. Int J Agric & Biol Eng, 2019; 12(1): 1–5.

Evaluation of Five Methods to Measure Normalized Difference Vegetation Index (NDVI) in Apple and Citrus

ABSTRACTNormalized Difference Vegetation Index (NDVI) is a common remote sensing calculation used to assess green biomass in addition to nutrient, pest, and water stress. The approach used in NDVI has potential for a broader use of remote sensing of plant physiological status. The purpose of the study was to evaluate techniques to that utilize combined near-infrared (NIR) and visible detection by low cost web cameras in comparison with a dedicated NDVI sensor and direct measurement of red-green-blue (RGB) and NIR bands to calculate NDVI and identify water stress. Established techniques as well as new techniques for NDVI computation from cameras were evaluated and compared to soil plant analysis development (SPAD) chlorophyll meter values to assess the accuracy of NDVI for predicting plant stress in apple and citrus. NDVI is a useful tool when evaluating long-term crop changes such as pest damage, chronic water shortage, and nutrient deficiencies that affect chlorophyll but NDVI is not useful for acute stresses such as an irrigation pump failure or plugged irrigation lines that have an effect within days.

Webcam network and image database for studies of phenological changes of vegetation and snow cover in Finland, image time series from 2014 to 2016

Abstract. In recent years, monitoring of the status of ecosystems using low-cost web (IP) or time lapse cameras has received wide interest. With broad spatial coverage and high temporal resolution, networked cameras can provide information about snow cover and vegetation status, serve as ground truths to Earth observations and be useful for gap-filling of cloudy areas in Earth observation time series. Networked cameras can also play an important role in supplementing laborious phenological field surveys and citizen science projects, which also suffer from observer-dependent observation bias. We established a network of digital surveillance cameras for automated monitoring of phenological activity of vegetation and snow cover in the boreal ecosystems of Finland. Cameras were mounted at 14 sites, each site having 1–3 cameras. Here, we document the network, basic camera information and access to images in the permanent data repository (http://www.zenodo.org/communities/phenology_camera/). Individual DOI-referenced image time series consist of half-hourly images collected between 2014 and 2016 (https://doi.org/10.5281/zenodo.1066862). Additionally, we present an example of a colour index time series derived from images from two contrasting sites.

On-the-go Image Processing System for Spatial Mapping of Lettuce Fresh Weight in Plant Factory

Real-time monitoring of crop growth parameters in plant factory can provide useful information about accurate assessment of their growth status for precision crop management. Plant weight is one of the most important biophysical properties used to determine the optimum time for harvesting. Conventional plant weight measurements are destructive and laborious. An on-the-go image processing system consisting of image acquisition and weight estimation was developed to generate a fresh weight map of plants grown in hydroponic solutions. Key technologies developed in this study are real-time image processing and spatial mapping methods that estimate the fresh weights of individual lettuces. Images were automatically captured with a low cost web camera and processed using a MYRIO-based embedded controller. The camera and embedded system moved along an XY axis frame above a plant growing bed (0.94 x 1.8 m) using two stepping motors and linear actuators. The image preprocessing algorithm consisted of two main subroutines, i.e., image segmentation and target plant recognition. For the image segmentation, the S channel of the HSV color space and Otsu’s threshold were used to separate the plants from the background. The target plant was identified based on location information of the growing bed holes in captured images. The plant weight was estimated using calibration equations previously developed that relate the pixel numbers of lettuce images to their actual fresh weights in conjunction with the use of a two-point normalization method. The accuracy of the fresh weight determined by the developed embedded system was confirmed by a highly linear relationship with a slope near 1.0 and a coefficient of determination (R2) of 0.95 with a processing time of within 4 s. In addition, it was possible to generate a spatial map of the fresh weights of lettuces grown in a cultivation bed, which could be used to estimate their yields prior to harvesting.

Traffic Signal Control Using Web-Camera and Traffic Flow Estimation

Due to rapid urbanization, large cities in developing countries have problems with heavy traffic congestion. International aid is being provided to construct modern traffic signal infrastructure. But often such an infrastructure does not work well due to the high operating and maintenance costs and the limited knowledge of the local engineers. In this paper, we propose a signal control framework that uses image analysis to estimate traffic flows. It requires only low-cost Web cameras to support a signal control strategy based on the current traffic volume. We can estimate the traffic volumes of the roads near the traffic signals from a few observed points and then adjust the signal control. Through numerical experiments, we confirmed that the proposed framework can reduce an average travel time 20.6% compared to a fixed-time signal control. And the system also involves ideas for road crossing and ambulances.

Three Hundred and Sixty Degree Real-Time Monitoring of 3-D Printing Using Computer Analysis of Two Camera Views

Prosumer (producing consumer)-based desktop additive manufacturing has been enabled by the recent radical reduction in 3-D printer capital costs created by the open-source release of the self-replicating rapid prototype (RepRap). To continue this success, there have been some efforts to improve reliability, which are either too expensive or lacked automation. A promising method to improve reliability is to use computer vision, although the success rates are still too low for widespread use. To overcome these challenges an open source low-cost reliable real-time optimal monitoring platform for 3-D printing from double cameras is presented here. This error detection system is implemented with low-cost web cameras and covers 360 degrees around the printed object from three different perspectives. The algorithm is developed in Python and run on a Raspberry Pi3 mini-computer to reduce costs. For 3-D printing monitoring in three different perspectives, the systems are tested with four different 3-D object geometries for normal operation and failure modes. This system is tested with two different techniques in the image pre-processing step: SIFT and RANSAC rescale and rectification, and non-rescale and rectification. The error calculations were determined from the horizontal and vertical magnitude methods of 3-D reconstruction images. The non-rescale and rectification technique successfully detects the normal printing and failure state for all models with 100% accuracy, which is better than the single camera set up only. The computation time of the non-rescale and rectification technique is two times faster than the SIFT and RANSAC rescale and rectification technique.

ENGLISH

Nowadays the demands on video surveillance systems are rapidly increasing. Commercial spaces, universities and hospitals, warehouses require of a video capturing system that have ability to alert and record beside live video of the intruder. The current technologies require RFIDs which are costly and hence the security domain in all becomes expensive. Existing solutions have to assist their users for a wide variety of orthogonal designed based on a real time operating system that is raspbian as an exemplar. In this project video surveillance system is presented based on a single board computer represented by Raspberry PI as an embedded solution. The aim is to make a smart surveillance system which can be monitored by owner remotely. As it is connected with the system with IOT, system will send the notifications when an intrusion is detected inside the room. It is required to develop and implement and affordable low cost web-camera based surveillance system for remote security monitoring. Authorized user can access to their monitoring system remotely via internet with the use a mobile phone and monitor the situation on application. This entire work is done on raspberry pi with Raspbian operating system ported on it.

How to Build Your Simple and Cost-effective Arthroscopic Skills Simulator

Acquisition of arthroscopic skills is not always easy and can be time-consuming. Simulation in arthroscopy improves surgical skills and can bridge the gap between reduced surgical exposure and training time and the need to reach a required level of competency. We propose a method to create a simple and cost-effective arthroscopic skills simulator using readily available materials and a low-cost Web camera available from online shops. This arthroscopic simulation device can be used to improve skills of all levels of trainees at home, in a bioskills laboratory, or in the theater. It can also be used by experienced surgeons to train with instruments and devices before using them for the first time in theaters, thus ensuring safe use and improving patient safety. Further validation as a training tool is needed and should be the focus of additional research, but early results are very promising.

동심원 추적 알고리즘을 사용한 손가락 동작 인식

In this paper, we propose a novel algorithm, Concentric-Circle Tracing algorithm, which recognizes finger's shape and counts the number of fingers of hand using low-cost web-camera. We improve algorithm’s usability by using low-price web-camera and also enhance user's comfortability by not using a additional marker or sensor. As well as counting the number of fingers, it is possible to extract finger's shape information whether finger is straight or folded, efficiently. The experimental result shows that the finger gesture can be recognized with an average accuracy of 95.48%. It is confirmed that the hand-gesture is an useful method for HCI input and remote control command.

Double Lighting Machine Vision System to Monitor Harvested Paddy Grain Quality during Head-Feeding Combine Harvester Operation

A machine vision system to evaluate harvested paddy grain quality during harvesting using double lighting was developed. The prototype consisted of a low-cost web camera and two lighting systems: a ring white LED for front lighting, and a flat dome white LED light for backlighting. Both lighting systems were arranged in a coaxial axis, making the system simple, compact and easy to handle. The aim of the system is to analyse the captured images and determine the amount of unwanted materials (rachis branch, grass and leaves, and stems) and damaged grain (brown and crack rice) present in the paddy as it is being harvested. In this paper, we introduce the first step in the development of the system: the design and selection of components to optimize the performance of the system to monitor harvested paddy grain quality. The idea would be to mount the system on top of the inlet channel of the grain tank of a combine harvester to provide real-time assessment of harvesting operational parameters.

A Low Cost Pupillometry Approach

The need for low-cost health monitoring is increasing with the continuous increase of the elderly population. In this context, unobtrusive audiovisual monitoring methods can be of great importance. More particularly, the diameter of the pupil is a valuable source of information, since, apart from pathological cases, it can reveal the emotional state, the fatigue and the ageing. To allow for unobtrusive monitoring to gain acceptance, one should seek for efficient methods of monitoring using common low-cost hardware. This paper describes a method for monitoring pupil sizes using a common, low-cost web camera in real time. The proposed approach detects the face and the eyes area at first stage. Subsequently, optimal iris and sclera location and radius, modeled as ellipses, are found using efficient spatial filtering. As a final step, the pupil center and radius is estimated by optimal filtering within the area of the iris. Experimental results show both the efficiency and the effectiveness of our approach.