Image Processing-based System Research Articles

Machine Learning and Image Processing-Based System for Identifying Mushrooms Species in Malaysia

Malaysia, a country with a tropical climate characterized by consistent warmth and year-long high humidity, houses the perfect conditions for mushroom growth. Recently, there has been a surge in back-to-nature activities in Malaysia. However, many participants lack prior knowledge about the local flora and fungi, leading to a rise in mushroom poisoning cases, some of which have been fatal. Despite thorough research, there is a notable lack of identification studies specifically focused on mushroom species in Malaysia. Identifying these species is crucial for medical providers to effectively counteract the toxins from ingested mushrooms and also serves as an important educational tool. This study aims to determine the most suitable architecture for mushroom identification, focusing specifically on mushroom species found in Malaysia. A dataset of these mushrooms was curated, augmented, and processed through multiple variants of Vision Transformers (ViTs) and ResNet models, with uniform hyperparameters to ensure fairness. The results indicate that the ViT-L/16 model achieved the highest accuracy at 90.47%.

Plant Disease Classification Using Image Processing Technique

Agriculture remains pivotal to our economy, with farming playing a central role in revenue generation. Challenges such as pests, plant diseases, and evolving climate patterns pose threats to crop yield and production. Addressing these challenges, timely and accurate detection of plant diseases emerges as imperative. Manual detection, however, remains resource-intensive and often lags. Addressing this gap, this project proposes an innovative image processing-based system for rapidly detecting plant diseases. The system proficiently identifies specific diseases by analyzing images of plant leaves against a curated dataset. The emphasis of this study was on three major diseases: Bacterial Blight (with an accuracy of 98.6%), Alternaria Alternata (98.5714%), and Cercospora Leaf Spot (97.5%). The compelling results underline the system's capacity to swiftly and effectively categorize diseases, offering monoculture farmers an indispensable tool for obtaining prompt, disease-specific insights.

CattNIS: Novel identification system of cattle with retinal images based on feature matching method

Small and large farm animals are frequently identified using techniques like microchipping or tagging. However, these techniques may have a detrimental impact on animal welfare and may result in theft and fraud. Modern and sophisticated identification procedures using biometrics, including retina, face, and iris recognition, are available and have less of a detrimental effect on animal welfare. Because biometric markers are unchangeable and unaffected, it is feasible to stop situations of theft and fraud. This study uses retina images to identify animals based on each animal's different and specific retinal patterns. To the best of our knowledge, there isn't a publicly accessible dataset for images of animal retinas. The three main goals of this study are to: 1) produce a novel dataset of Cattle retinal images and make it publicly available; 2) create a successful image processing-based system for retinal identification and recognition; and 3) create a graphical user interface (GUI) that enables querying the database to ascertain whether a specific Cattle retina is present. To accomplish these goals, a dataset comprising 2430 images from the left and right eyes of 300 cattle was produced. After the dataset was created, the animal retinal images were segmented using image processing methods such as scaling, color transformations, image sharpening, contrast enhancements, noise filtering, and histogram equalization. By using the segmented images with the SURF, FAST, BRISK, and HARRIS techniques, distinctive retinal features were found. During the identification phase, the animal whose retinal characteristic matched the sought animal's retina the closest was recognized. Within the developed CattNIS system, the SURF approach has the highest accuracy rate of 92.25 percent. The results of this study show that the identification of cattle using retinal vascular patterns is highly successful. The availability of the collected cattle retinal images from this study is expected to significantly contribute to the advancement of future research in this field, both in terms of quantity and quality.

Enhancing Security Mechanisms for IoT-Fog Networks

This study contributes to improving Morocco's fish canning industry by integrating artificial intelligence (AI). The primary objective involves developing an AI and image processing-based system to monitor and guarantee canning process quality in the facility. It commenced with an IoT-enabled device capable of capturing and processing images, leading to the creation of an AI-driven system adept at accurately categorizing improperly crimped cans. Further advancements focused on reinforcing communication between IoT devices and servers housing individual client's neural network weights. These weights are vital, ensuring the functionality of our IoT device. The efficiency of the IoT device in categorizing cans relies on updated neural network weights from the Fog server, crucial for continual refinement and adaptation to diverse can shapes. Securing communication integrity between devices and the server is imperative to avoid disruptions in can classification, emphasizing the need for secure channels. In this paper, our key scientific contribution revolves around devising a security protocol founded on HMAC. This protocol guarantees authentication and preserves the integrity of neural network weights exchanged between Fog computing nodes and IoT devices. The innovative addition of a comprehensive dictionary within the Fog server significantly bolsters security measures, enhancing the overall safety between these interconnected entities.

Smart Parking System Based on Image Processing

Abstract: Successful implementation of smart parking systems can effectively reduce a lot of problems related to traffic congestion in urban areas. Wastage of fuel and time in search of a vacant parking space will be significantly reduced and the complete utilization of the available parking areas can make our cities really smart. Parking slot detection and user notification are the two major sections of a smart parking system. The empty parking space detection was initially done by deploying a number of sensors in the parking lot. It is highly expensive and complicated to install. But the advancement of image processing has enabled us to use images of the parking area to find out empty spaces. In this paper, a comparative study of the various parking space identification techniques has been done. Also, image processing-based system models have been presented as a replacement for sensor-based systems. A very efficient and simple technique for parking slot identification based on optical character recognition (OCR) has been introduced in this paper. The camera installed in the parking lot captures the image of the parking area with specially numbered parking spaces. The OCR system recognizes numbers that are not hidden by a vehicle parked over it and this information is used for identifying the empty spaces.

Smart Parking System Based On Image Processing

Abstract: Successful implementation of smart parking systems can effectively reduce a lot of problems related to traffic congestion in urban areas. Wastage of fuel and time in search of a vacant parking space will be significantly reduced and the complete utilization of the available parking areas can make our cities really smart. Parking slot detection and user notification are the two major sections of a smart parking system. The empty parking space detection was initially done by deploying a number of sensors in the parking lot. It is highly expensive and complicated to install. But the advancement of image processing has enabled us to use images of the parking area to find out empty spaces. In this paper, a comparative study of the various parking space identification techniques has been done. Also, image processing-based system models have been presented as a replacement for sensor-based systems. A very efficient and simple technique for parking slot identification based on optical character recognition (OCR) has been introduced in this paper. The camera installed in the parking lot captures the image of the parking area with specially numbered parking spaces. The OCR system recognizes numbers that are not hidden by a vehicle parked over it and this information is used for identifying the empty spaces.

Crack Identification and Trajectory Planning for Automatic Gas Metal Arc Welding

Gas metal arc welding process has the capability of producing high quality, all position welding, and is easily adaptable for automated welding applications. Repair welding of random cracks on existing assembly/structure through automatic welding would need real time crack/gap identification and weld path generation. In this work, an image processing-based system is presented for identifying the crack geometry. Graphical user interfaces are also developed to take necessary user inputs required at different stages for crack identification, predicting weld bead dimension, and weld path generation. Based on the identified crack geometry and predicted weld bead feature, linear and curved weld path planning methodology is proposed. The proposed modules are validated for a case study by successfully generating the desired weld paths. Different natures of velocity profiles are considered to appraise the role on motion behaviour and a suitable profile is selected for reducing the jerks at sharp corners/via points on the weld path and maintaining uniform bead geometry.

Klasifikasi Down Syndrome Menggunakan Tekstur LBP dengan Tiga Variasi Distance Classifiers

Down syndrome is the most easily identified and most common genetic disorder. Down syndrome variation of trisomy 21 has characteristics that are common to people with Down syndrome. But, children with this Down syndrome variation have slight differences in the mouth and appearance because they tend to have similar oral features with their parents and siblings so that distinguishing them is quite complex. Therefore, it is important to know in more details the special characteristics of children with Down syndrome. This study aims to create an innovative image processing-based system so that it is more practical to classify people with down syndrome and normal people. The method that can be used is the Local Binary Pattern (LBP) method. This study used 2400 mouth frames of children with Down syndrome and normal children for data training. Then, it also uses 3600 mouth frames for children with Down syndrome and normal children for testing data. The results obtained are the threshold value which gives a good classification of 0.1-0.2 for the three variations of the distance calculation method. The Euclidean and Chebychev methods have an accuracy quality of 100% while the City block method at the threshold of 0.1-0.2 has an accuracy of 91.6. So, it can be said that the most accurate method in this research is the Chebychev, Euclidean, then City Block method.

An image processing-based system proposal for real-time detection of drowsiness from a vehicle driver's eye movements

Drowsiness is one of the underlying causes of driving accidents leading to serious injuries and deaths annually. According to the experts has mentioned that almost 30% of all traffic accidents have been caused by drowsiness. In avoiding these traffic accidents, a proper system is required to prevent the driver from falling asleep. This study proposes a real-time image processing-based system for recognizing the drowsiness face expression of the vehicle driver. The method of this study, detecting the exact position facial landmarks and both left and right eyes using dlib and eye aspect ratio algorithm. This system, after detecting drowsiness eye, give audible alert the vehicle driver to stay awake throughout the driving journey.

Image-processing based facial imperfection region detection and segmentation

As interest in skin-beauty has recently increased, the consumption of skin-related products has steadily increased. Visiting a professional medical institution or clinic to determine one’s skin condition(s) for self-management is not only costly but also cumbersome. It is also difficult to recognize small changes in the skin when an individual is managing the skin. To solve these problems, we suggest a new image-processing based system that detects facial imperfection regions such as wrinkles, moles, etc. of someone’s facial photographs taken with normal cameras rather than with equipment such as expensive skin microscopes. When the facial photograph is input to our system, the LAB color model is used to correct the illumination effect, and then only the skin region of the face is extracted by using the range of the skin region of the HSV and YCbCr color models as well as by the K-Means algorithm. After the facial region is extracted, the Gabor filter is processed to detect the reactivity of discontinuity. Finally, the density-based spatial clustering of applications with noise algorithm is processed to classify areas of wrinkles, spots, and other skin diseases. Experiments on various images showed that the proposed algorithm showed a robust response to the distortion caused by the illumination and obtained remarkable results. Our method allows the user to check the skin condition of images taken in real life.

Impact of progressive yarn extension on angular fibre orientation

A detailed analysis has been carried out to assess the influence of progressive yarn extension on angular fibre orientation. Analysis of 3-D trajectory of the fibre using multi coloured tracer fibre technique was carried out using specially designed microcontroller based instrument and image processing-based system. The angular orientation of fibre in the ring spun yarn has been visualised in terms of migration angle, helix angle, spatial orientation angle and three other angles defined on the basis of 3-D path of fibre. Average value of migration angle, helix angle and spatial orientation angle continuously decreases with the increase in yarn extension. The angles defined on the basis of 3-D path of fibre responded differently to yarn extension. But the value of angle related to the direction cosines defined as ratio of segmental radius to fibre element length hovers around 90° at all yarn extension levels. This confirms that model of symmetric migration remains unaffected by axial yarn extension.

BİR ENDÜSTRİYEL ROBOTUN KAFESSİZ ÇALIŞMASINI SAĞLAYACAK GÖRÜNTÜ TABANLI GÜVENLİK SİSTEMİNİN GELİŞTİRİLMESİ

İnsan ve robotun birlikte çalışması ile yaratılan işbirliği sayesinde, üretimde esneklik, verimlilik, kapasite ve kalite arttırılabilmektedir. Bu birlikte çalışma formunda (insan-robot etkileşiminde) insan ve robotun aynı ortamı paylaşması; çarpma/çarpışma olasılığını ortaya çıkardığından, bu türden çalışma ortamlarında güvenliği sağlamak en önemli sorun olmaktadır. Geleneksel robotlu sistemlerde güvenlik; kafes sistemleri, lazer tarayıcılar, ışık bariyerleri gibi çeşitli donanımlarla sağlanmaktadır. Ancak bu sistemler çalışma ortamını kısıtlamakta, insan ile robotun birlikte çalışmasında avantaj olarak öne sürülen esneklik faktörünü ortadan kaldırmaktadır. Bu çalışmanın ana amacı, kafes ve diğer önleyici sistemleri ortadan kaldırabilmek için robot üzerine konumlandırılan kamera ve donanımların kullanıldığı görüntü işleme tabanlı bir sistem ve algoritma ortaya çıkarabilmektir. Kullanılan kamera ve donanımların ihtiyaçları sağlayacak nitelikte, ancak düşük maliyetli standart donanımlardan seçilmesine dikkat edilmiştir. Böylelikle standart endüstriyel robotların etkileşime uygun formda çalıştırılabilmeleri için düşük maliyetli ve kolay uygulanabilir bir çözüm yaratılmaya çalışılmıştır. Bu amaçla; çalışmada, öncelikle çoklu kamera konumlandırılması ve görüntü elde edilmesi üzerine geliştirilen yöntem açıklanmıştır. Ardından elde edilmiş çoklu görüntülerden tekil bir panoramik görüntü elde edilmesi ve bu veri üzerinden insan/nesne tespiti ile konum ve mesafe çıkarımının nasıl yapıldığı belirtilmiştir. Bulunan parametreler ve tanımlı güvenlik kurallarını kullanarak insan ve robotun aynı ortamda çalışmasını düzenleyen algoritma özetlenmiş, son olarak da geliştirilen sistemin getirmiş olduğu kazanım ele alınmış ve ileride yapılabilecek çalışmalara ilişkin öngörüler verilmiştir.

A Smart Image Processing-based System for Parking Space Vacancy Management

A Smart Image Processing-based System for Parking Space Vacancy Management

A novel image processing-based system for turbidity measurement in domestic and industrial wastewater.

Wastewater treatment facilities are continually challenged to meet both environmental regulations and reduce running costs (particularly energy and staffing costs). Improving the efficiency of operational monitoring at wastewater treatment plants (WWTPs) requires the development and implementation of appropriate performance metrics; particularly those that are easily measured, strongly correlate to WWTP performance, and can be easily automated, with a minimal amount of maintenance or intervention by human operators. Turbidity is the measure of the relative clarity of a fluid. It is an expression of the optical property that causes light to be scattered and absorbed by fine particles in suspension (rather than transmitted with no change in direction or flux level through a fluid sample). In wastewater treatment, turbidity is often used as an indicator of effluent quality, rather than an absolute performance metric, although correlations have been found between turbidity and suspended solids. Existing laboratory-based methods to measure turbidity for WWTPs, while relatively simple, require human intervention and are labour intensive. Automated systems for on-site measuring of wastewater effluent turbidity are not commonly used, while those present are largely based on submerged sensors that require regular cleaning and calibration due to fouling from particulate matter in fluids. This paper presents a novel, automated system for estimating fluid turbidity. Effluent samples are imaged such that the light absorption characteristic is highlighted as a function of fluid depth, and computer vision processing techniques are used to quantify this characteristic. Results from the proposed system were compared with results from established laboratory-based methods and were found to be comparable. Tests were conducted using both synthetic dairy wastewater and effluent from multiple WWTPs, both municipal and industrial. This system has an advantage over current methods as it provides a multipoint analysis that can be easily repeated for large volumes of wastewater effluent. Although the system was specifically designed and tested for wastewater treatment applications, it could have applications such as in drinking water treatment, and in other areas where fluid turbidity is an important measurement.

Influence of yarn extension on longitudinal structural characteristics of fibres

A detailed study on the effect of progressive yarn extension on longitudinal structural characteristics of fibres has been reported in the present paper. An image processing-based system to characterise and visualise the configuration of fibres in yarn under extended mode has been adopted. A detailed study has been reported on the changes in longitudinal structural characteristics of fibres, such as fibre extent, fibre strain, straightening and slippage of fibres, fibre-pair-overlap length (FPO) and fibre-overlap index (FPI), during axial extension of ring-spun yarn. The actual length of fibre and the fibre extent in the yarn were found to be increased with the increase in yarn extension. With yarn extension from 0 to 4% the fibre strain in the yarn becomes moderate and further increase in yarn extension from 4 to 8% the fibre strain reduces, but with further increase in yarn extension of 8–12% the fibre strain again increases. It has also been observed that the fibre straightening percentage at different intervals of yarn extension is not the same and it decreases with the increase in yarn extension. The fibre slippage percentage is also not the same at different intervals of yarn extension percentage. It is found to be maximum at 4–8% and minimum at 0–4% yarn extension.

Effect of yarn extension on migration parameters of fibres in yarns

The present paper deals with a detailed study on the effect of progressive yarn extension on fibre migration. An image processing-based system to characterise and visualise the configuration of fibres in yarn in extended mode has been adopted. A detailed study has been reported on the changes in migration parameters, like mean fibre position (MFP), mean migration intensity (MMI) and root mean square deviation (RMSD), during axial extension of ring-spun yarn. It has been observed that the fibre migration parameters, like MFP, MMI and RSMD, are significantly affected by extension of ring-spun yarn and the trends are different for different levels of extension. It has been observed that the mean fibre position (MFP) increases slightly up to 4% of yarn extension and then decreases up to 8% extension but with a further increase of yarn extension up to 12%, the value of MFP slightly increases again. It has also been reported that with the increase of yarn extension the average RMSD and MMI.