Automatic Recognition System Research Articles

Underwater Small Target Classification Using Sparse Multi-View Discriminant Analysis and the Invariant Scattering Transform

Sonar automatic target recognition (ATR) systems suffer from complex acoustic scattering, background clutter, and waveguide effects that are ever-present in the ocean. Traditional signal processing techniques often struggle to distinguish targets when noise and complicated target geometries are introduced. Recent advancements in machine learning and wavelet theory offer promising directions for extracting informative features from sonar return data. This work introduces a feature extraction and dimensionality reduction technique using the invariant scattering transform and Sparse Multi-view Discriminant Analysis for identifying highly informative features in the PONDEX09/PONDEX10 datasets. The extracted features are used to train a support vector machine classifier that achieves an average classification accuracy of 97.3% using six unique targets.

Balancing validity and reliability as a function of sampling variability in forensic voice comparison

In forensic comparison sciences, experts are required to compare samples of known and unknown origin to evaluate the strength of the evidence assuming they came from the same- and different-sources. The application of valid (if the method measures what it is intended to) and reliable (if that method produces consistent results) forensic methods is required across many jurisdictions, such as the England & Wales Criminal Practice Directions 19A and UK Crown Prosecution Service and highlighted in the 2009 National Academy of Sciences report and by the President’s Council of Advisors on Science and Technology in 2016. The current study uses simulation to examine the effect of number of speakers and sampling variability and on the evaluation of validity and reliability using different generations of automatic speaker recognition (ASR) systems in forensic voice comparison (FVC). The results show that the state-of-the-art system had better overall validity compared with less advanced systems. However, better validity does not necessarily lead to high reliability, and very often the opposite is true. Better system validity and higher discriminability have the potential of leading to a higher degree of uncertainty and inconsistency in the output (i.e. poorer reliability). This is particularly the case when dealing with small number of speakers, where the observed data does not adequately support density estimation, resulting in extrapolation, as is commonly expected in FVC casework.

A Sign Language Recognition using Improved Grey Wolf Optimization based neural networks

Sign language was developed to enable the deaf and hard of hearing community to communicate with society and convey information. It is the primary means by which they can interact with each other, as well as with the general population. The automatic gesture recognition system described in this paper uses Gray Wolf Optimization (GWO) to identify the most relevant features from a set of gesture images, with the aim of improving model performance. The main steps of the system are explained, including image preprocessing, feature extraction, GWO-based feature selection, classifier training, and evaluation. The proposed model achieved an accuracy of 99.9%.

Quality Assessment of Rice Using Convolution Neural Networks and Other Machine Learning Techniques- A Comparative Study

Rice holds its position as the most widely cultivated crop worldwide, its demand steadily rising alongside population growth. The precise identification of grains, especially wheat and rice, holds significant importance as their ultimate utilization depends on the quality of grains prior to processing. Traditionally, grain identification tasks have been predominantly manual, relying on experienced grain inspectors and consuming considerable time. However, this manual classification process is susceptible to variations influenced by individual perception, given the subjective nature of human image interpretation. Consequently, there is an urgent need for an automated recognition system capable of accurately identifying grains under diverse environmental conditions, necessitating the application of digital image processing techniques. In this study, we focus on grading five distinct varieties of rice based on their quality, employing a range of convolution neural networks (CNN) namely, Efficientnetb0, Googlenet, MobileNetV2, Resnet50, Resnet101, and ShuffleNet. The performance of CNN towards identification and grading of rice grain is also compared to that of other parametric and Non-parametric classifiers namely, Linear Discriminant Analysis (LDA), K-Nearest Neighbor (K-NN), Naive Bayes (NB), and Back Propagation Neural Network (BPNN) using Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) based texture features. The image dataset comprises five grades of rice, each containing 100 images, resulting in a comprehensive collection of 500 samples for analysis. It is observed that, Convolution neural networks can grade five different qualities of rice with highest accuracy of 64.4% in case of GoogleNet. Results show that, rice grading using texture features performs better with highest accuracy of 99.2% (using GLCM) and 93.4% (using GLRLM).

MoSFPAD: An end-to-end ensemble of MobileNet and Support Vector Classifier for fingerprint presentation attack detection

Automatic fingerprint recognition systems are the most extensively used systems for person authentication although they are vulnerable to Presentation attacks. Artificial artifacts created with the help of various materials are used to deceive these systems causing a threat to the security of fingerprint-based applications. This paper proposes a novel end-to-end model to detect fingerprint Presentation attacks. The proposed model incorporates MobileNet as a feature extractor and a Support Vector Classifier as a classifier to detect presentation attacks in cross-material and cross-sensor paradigms. The feature extractor’s parameters are learned with the loss generated by the support vector classifier. The proposed model eliminates the need for intermediary data preparation procedures, unlike other static hybrid architectures. The performance of the proposed model has been validated on benchmark LivDet 2011, 2013, 2015, 2017, and 2019 databases, and overall accuracy of 98.64%, 99.50%, 97.23%, 95.06%, and 95.20% are achieved on these databases, respectively. The performance of the proposed model is compared with state-of-the-art methods and is able to reduce the average classification error of 3.63%, 1.86%, 1.83%, 0.05%, 0.93% on LivDet 2011, 2013, 2015, 2017, and 2019 databases, respectively for same and cross material protocols in intra-sensor paradigm. The proposed method also reduced the average classification error of 1.59%, 1.41%, and 2.29% for LivDet 2011, 2013, and 2017 databases, respectively for the cross-sensor paradigm. It is evident from the results that the proposed method outperforms state-of-the-art methods in intra-sensor as well as cross-sensor paradigms in terms of average classification error.

276. AI NAVIGATION SURGERY FOR ESOPHAGEAL CANCER

Abstract With recent technological innovations, artificial intelligence (AI) has begun to be used not only in endoscopic diagnosis and pathology diagnosis but also in the field of surgery. AI enables objective and consistent recognition and evaluation, making it suitable for surgical assistance in surgical operations as well as for the evaluation of surgical techniques and learning curves. First, an AI-assisted surgical procedure recognition system for robotic esophagectomy was developed. By focusing on surgical procedures, the system automatically recognizes surgical procedures with 84% accuracy using AI. In particular, more than 90% of accuracy was obtained in the phase for Left RLN lymph node dissection and Post-dissection to completion of surgery. In addition, as intraoperative navigation, a system for automatic recognition of critical organs such as the recurrent laryngeal nerve, trachea, and subclavian artery was developed, and its recognition accuracy is gradually improving, enabling organ recognition in real time. Currently, with data from more than 100 cases, the system is able to predict Intersection over Union (IoU) with an accuracy of 0.3-0.4. Similar developments are underway in other areas, such as automatic recognition of the surgical process in robotic distal gastrectomy, and a surgical difficulty prediction system is being built using this technology. This system is expected to make it possible to automatically determine the degree of difficulty of surgery during the operation, leading to the decision to change the surgical team during the operation and the prediction of the operation completion time. These technologies can be useful not only for intraoperative navigation, but also surgical education and postoperative care. Using them, young surgeons have the opportunity to dive into authentic scenarios and acquire practical experience safely, bypassing the dangers inherent in real-life surgeries. Through the use of AI-powered interactive simulations, they can deeply grasp surgical methods, establishing the groundwork of crucial practical abilities needed for performing successful operations. In the future, we intend to verify the external validity of these systems using data from multiple facilities, and further work on the development of a surgical support system that transcends fields, with the aim of obtaining regulatory approval as a medical device. In addition to the potential clinical applications of the systems under development, we would like to discuss the current status and future of AI-based surgical support systems in the surgical field.

Automatic recognition system for concrete cracks with support vector machine based on crack features

Cracks are a common problem in concrete surfaces. With the continuous optimization of machine vision-based inspection systems, effective crack detection and recognition is the core of the entire system. In this study, support vector machine (SVM) was used to distinguish cracks from other regions. To complete the recognition system of the SVM, a framework consisting of an image processing and recognition model was proposed. An algorithm combining the Prewitt operator with the Otsu threshold was proposed for image segmentation. The binary image processed by the new algorithm combined with mathematical morphology can result in a more complete crack zone and fewer interference regions. After the initial parameter extraction, most of the impurity areas were screened by preliminary discrimination, removing 99% of the impurities. This processing step ensured the balance and effectiveness of the samples. To establish an automatic identification model based on SVM with a radial basis function, compactness, occupancy rate, and length–width ratio were selected as input parameters after comparing these three features with all six features of the crack. The recognition accuracy of this system reaches 97.14%, demonstrating that the proposed method is effective and satisfies practical requirements.

Do long-term acoustic-phonetic features and mel-frequency cepstral coefficients provide complementary speaker-specific information for forensic voice comparison?

A growing number of studies in forensic voice comparison have explored how elements of phonetic analysis and automatic speaker recognition systems may be integrated for optimal speaker discrimination performance. However, few studies have investigated the evidential value of long-term speech features using forensically-relevant speech data. This paper reports an empirical validation study that assesses the evidential strength of the following long-term features: fundamental frequency (F0), formant distributions, laryngeal voice quality, mel-frequency cepstral coefficients (MFCCs), and combinations thereof. Non-contemporaneous recordings with speech style mismatch from 75 male Australian English speakers were analyzed. Results show that 1) MFCCs outperform long-term acoustic phonetic features; 2) source and filter features do not provide considerably complementary speaker-specific information; and 3) the addition of long-term phonetic features to an MFCCs-based system does not lead to meaningful improvement in system performance. Implications for the complementarity of phonetic analysis and automatic speaker recognition systems are discussed.

Deep learning based System for automatic motorcycle license plates detection and recognition

Deep learning based System for automatic motorcycle license plates detection and recognition

StrawSnake: A Real-Time Strawberry Instance Segmentation Network Based on the Contour Learning Approach

Automated harvesting systems rely heavily on precise and real-time fruit recognition, which is essential for improving efficiency and reducing labor costs. Strawberries, due to their delicate structure and complex growing environments, present unique challenges for automated recognition systems. Current methods predominantly utilize pixel-level and box-based approaches, which are insufficient for real-time applications due to their inability to accurately pinpoint strawberry locations. To address these limitations, this study proposes StrawSnake, a contour-based detection and segmentation network tailored for strawberries. By designing a strawberry-specific octagonal contour and employing deep snake convolution (DSConv) for boundary feature extraction, StrawSnake significantly enhances recognition accuracy and speed. The Multi-scale Feature Reinforcement Block (MFRB) further strengthens the model by focusing on crucial boundary features and aggregating multi-level contour information, which improves global context comprehension. The newly developed TongStraw_DB database and the public StrawDI_Db1 database, consisting of 1080 and 3100 high-resolution strawberry images with manually segmented ground truth contours, respectively, serves as a robust foundation for training and validation. The results indicate that StrawSnake achieves real-time recognition capabilities with high accuracy, outperforming existing methods in various comparative tests. Ablation studies confirm the effectiveness of the DSConv and MFRB modules in boosting performance. StrawSnake’s integration into automated harvesting systems marks a substantial step forward in the field, promising enhanced precision and efficiency in strawberry recognition tasks. This innovation underscores the method’s potential to transform automated harvesting technologies, making them more reliable and effective for practical applications.

Application of artificial intelligence based on pattern recognition in music entertainment environment and automatic music recognition

The development of digital technology has given music dissemination a technological advantage of high productivity and efficiency. With the continuous improvement of digital technology and the diversification of communication forms, information visualization design pays more attention to the audience’s experience and feelings. This article analyzes the application of pattern recognition based artificial intelligence in music entertainment environments and automatic recognition of music. Digital media can achieve bidirectional information transmission through user participation and feedback, and can provide customers with more entertainment music experiences through entertainment games and other means. This article focuses on the image enhancement processing, note recognition, and automatic recognition conversion of music scores in complex contexts, and develops an automatic music score recognition system to digitize music score information. Through online dissemination, the public can fully experience the beauty of music in the process of communication and interaction, and at the same time, music can be brought into the entertainment environment.

Sensing Human Emotion using Emerging Machine Learning Techniques

Human emotion recognition using machine learning is a new field that has the potential to improve user experience, lower crime, and target advertising. The ability of today's emotion detection systems to identify human emotions is essential. Applications ranging from security cameras to emotion detection are readily accessible. Machine learning-based emotion detection recognises and deciphers human emotions from text and visual data. In this study, we use convolutional neural networks and natural language processing approaches to create and assess models for emotion detection. Instead of speaking clearly, these human face expressions visually communicate a lot of information. Recognising facial expressions is important for human-machine interaction. Applications for automatic facial expression recognition systems are numerous and include, but are not limited to, comprehending human conduct, identifying mental health issues, and creating artificial human emotions. It is still difficult for computers to recognise facial expressions with a high recognition rate. Geometry and appearance-based methods are two widely used approaches for automatic FER systems in the literature. Pre-processing, face detection, feature extraction, and expression classification are the four steps that typically make up facial expression recognition. The goal of this research is to recognise the seven main human emotions anger, disgust, fear, happiness, sadness, surprise, and neutrality using a variety of deep learning techniques (convolutional neural networks).

Smart Traffic Management: Enhancing Urban Mobility through Predictive Analysis and AI-Driven Solutions

In urban environments, the issue of unauthorized parking in designated no-parking zones persists, leading to traffic congestion and safety hazards. inaccurate license plate recognition, License plate (LP) detection is a crucial task for Automatic License Plate Recognition (ALPR) systems. Most existing LP detection networks can detect License plates, but their accuracy suffers when license plates (LPs) are tilted or deformed due to perspective distortion. This leading to difficulties in identifying vehicle owners. To address this challenge, this project present TraceMe, a predictive system utilizing advanced machine learning algorithms. The system employs YOLOv8 for efficient object detection, focusing on identifying vehicles in no-parking zones, and Tesseract OCR for accurate license plate recognition. The extracted license plate information is then processed by a machine learning model trained to predict the owner of the vehicle. The proposed system involves collecting and annotating a diverse dataset, training YOLOv8 and LPRNet model for vehicle number plate detection, utilizing Tesseract OCR for license plate extraction, and implementing a machine learning model for owner identification. Real-time processing and integration with surveillance systems allow for immediate identification of unauthorized parking incidents. The system generates alerts or notifications, aiding law enforcement in enforcing parking regulations.TraceMe not only provides a technological solution to mitigate unauthorized parking but also contributes to improved traffic management and public safety.

Smart Recognition and Authentication based on Haar Cascade Classifier and Visual Cryptography

The algorithm with an E-verification method is proposed that combines automated facial recognition technology with digital media and later it combines aspects of secret data exchange and visual cryptography. A face detection technique based on Internet of Things (IoT) selects features using the Principal Component Analysis (PCA) algorithm and the Haar cascade classifier. The selected face serving as a cover follows two level Discrete Haar Wavelet Transform. The shares generated from digital signature and fingerprint are diffused into the converted coefficients. Furthermore, the imperceptibility of the additional noise is increased by a bit-level noise reduction technique. Authenticity is confirmed by regenerating a message digest at the receiving end, and the extraction process operates in complete blindness. The approach is suitable for smart card design and may be used as an automatic recognition system in a real-world setting. Performance comparisons show a notable improvement over other approaches that are comparable. Additionally, the technique is effective against some related attacks.

Silent no more: a comprehensive review of artificial intelligence, deep learning, and machine learning in facilitating deaf and mute communication

People who often communicate via sign language are essential to our society and significantly contribute. They struggle with communication mostly because other people, who often do not understand sign language, cannot interact with them. It is necessary to develop a dependable system for automatic sign language recognition. This paper aims to provide a comprehensive review of the advancements in artificial intelligence (AI), deep learning (DL), and machine learning (ML) technologies that have been used to facilitate communication for individuals who are deaf and mute (D–M). This study explores various applications of these technologies, including sign language interpretation, speech recognition, and text-to-speech synthesis. By examining the current state of research and development in AI, ML, and DL for the D–M field, the survey sheds light on the potential and challenges faced in utilizing AI, deep learning, and ML to bridge the communication gap for the D–M community. The findings of this survey will contribute to a greater understanding of the potential impact of these technologies in improving access to communication for individuals who are D–M, thereby aiding in the development of more inclusive and accessible solutions.

Decoding faces: Misalignments of gender identification in automated systems

Automated Facial Analysis technologies, predominantly used for facial detection and recognition, have garnered significant attention in recent years. Although these technologies have seen advancements and widespread adoption, biases embedded within systems have raised ethical concerns. This research aims to delve into the disparities of Automatic Gender Recognition systems (AGRs), particularly their oversimplification of gender identities through a binary lens. Such a reductionist perspective is known to marginalize and misgender individuals. This study set out to investigate the alignment of an individual's gender identity and its expression through the face with societal norms, and the perceived difference between misgendering experiences from machines versus humans. Insights were gathered through an online survey, utilizing an AGR system to simulate misgendering experiences. The overarching goal is to shed light on gender identity nuances and guide the creation of more ethically responsible and inclusive facial recognition software.

Breaking the silence: empowering the mute-deaf community through automatic sign language decoding.

The objective of this study is to develop a system for automatic sign language recognition to improve the quality of life for the mute-deaf community in Egypt. The system aims to bridge the communication gap by identifying and converting right-hand gestures into audible sounds or displayed text. To achieve the objectives, a convolutional neural network (CNN) model is employed. The model is trained to recognize right-hand gestures captured by an affordable web camera. A dataset was created with the help of six volunteers for training, testing, and validation purposes. The proposed system achieved an impressive average accuracy of 99.65 % in recognizing right-hand gestures, with high precision value of 95.11 %. The system effectively addressed the issue of gesture similarity between certain alphabets by successfully distinguishing between their respective gestures. The proposed system offers a promising solution for automatic sign language recognition, benefiting the mute-deaf community in Egypt. By accurately identifying and converting right-hand gestures, the system facilitates communication and interaction with the wider world. This technology has the potential to greatly enhance the quality oflife for individuals who are unable to speak or hear, promoting inclusivity and accessibility.

Enhancing ANPR accuracy for staff car verification using convolutional neural networks and RegEX correction

This research paper presents the development and evaluation of an Automatic Number Plate Recognition (ANPR) system using Convolutional Neural Networks (CNN) with Regex correction. The aim is to enhance the accuracy and effectiveness of car verification and security processes at First Technical University, Ibadan. The ANPR system was implemented both without Regex correction and with Regex correction. The evaluation results demonstrate significant improvements in the system's performance when CNN with Regex correction is employed. The CNN-based ANPR system achieves a precision of 1.00, recall of 0.90, and F1-score of 0.95 in accurately identifying number plates. These scores indicate increased accuracy and reduce false positives compared to the system without Regex correction. The integration of CNN and Regex correction effectively handles variations and errors in the number plate data, leading to a reliable and efficient car verification process. Future work can focus on further refining the CNN model and optimizing the Regex correction algorithms to enhance the system's accuracy and robustness. The developed ANPR system, utilizing CNN with Regex correction, shows great potential for enhancing car verification and security in various domains, including law enforcement, parking management, and traffic monitoring

Enhancing Bangladeshi License Plate Recognition: A YOLOv8 Approach with Roboflow Integration for Accuracy and Speed Optimization

Abstract: Automatic License Plate Recognition (ALPR) systems are indispensable in modern transportation services, offering crucial benefits in traffic management, parking, toll collection, and surveillance. However, implementing ALPR in Bangladesh presents challenges due to the intricacies of Bangla characters and low-resolution CCTV images. Despite ALPR's potential to enhance safety and security, issues such as license plate variations and image quality hinder its effective implementation. This research addresses these challenges by developing a customized YOLOv8 model tailored for recognizing Bengali license plates, with a focus on precise localization, character segmentation, and script deciphering. Leveraging advanced deep learning techniques, the study aims to enhance efficiency and accuracy for applications in law enforcement and traffic management. Through a comprehensive workflow integrating Roboflow and YOLOv8, effective dataset collection, annotation, augmentation, and model training are demonstrated. System evaluation on diverse test datasets confirms the reliable detection and recognition of Bangladeshi license plates, underscoring the model's practical utility and robustness in real-world scenarios. Additionally, the research showcases notable advancements in ALPR technology specific to Bangladesh's unique context. Noteworthy is the model's exceptional performance in detecting license plates from corner angles, even across various vehicle types, as well as its ability to accurately identify plates amid challenging conditions such as broken or obscured plates and low-resolution images. Moreover, the system's proficiency in scenarios with multiple license plates, like those on buses adorned with banners, contributes significantly to improved road safety and traffic management.

Medicinal Herb Identification Using CNN Model

Abstract: Automated herb identification plays a crucial role in various industries such as cosmetics, medicine, and food, where the need to accurately identify different plant species is essential. However, existing methods often face challenges when dealing with complex backgrounds and a wide variety of patterns, especially in wild environments. In response to these challenges, we propose an innovative convolutional neural network (CNN) model that incorporates two key components: the Part- Information Perception Module and the Species Classification Module. The Part-Information Perception Module in our model is designed to focus the model's attention on the relevant parts of the herb, effectively suppressing background noise. This mechanism allows the model to better discern the distinctive features of the herb itself, improving overall accuracy. Furthermore, we employ depthwise separable convolution and label smoothing techniques to reduce model complexity and minimize the impact of labelling inconsistencies, enhancing both efficiency and reliability. To validate the effectiveness of our model, we conducted experiments using a diverse dataset of herb images. The results demonstrate a significant improvement in accuracy and model efficiency compared to existing methods, making it a valuable tool for herb identification in challenging environments. In addition to herb identification, we also address the complex task of recognizing medicinal plant species. To facilitate this, we have created a dataset comprising image features of plant leaves, enabling the development of an automated recognition system using machine learning classifiers. Our experiments show that the classifiers achieve an impressive average accuracy rate of over 97%, highlighting the potential of our approach in accurately identifying medicinal plant species.