Faster Region-based Convolutional Neural Network Research Articles

Dental caries detection using faster region-based convolutional neural network with residual network

Dental caries is the highest prevalent dental disease in the world by 2022. Caries can be stopped by early detection of patients through efficient screening. Previously, there have been several methods used to detect caries such as single shot multibox detector (SSD), faster region-based convolutional neural network (Faster R-CNN) and you only look once (YOLO). This research aims to develop accurate dental caries detection using Faster R-CNN. Using a dataset collected from scraping on the internet, this research is started by creating an original dataset consisting of 81 base images which are then augmented to a total of 486 images and annotated by dental health experts from Jenderal Soedirman University. Transfer learning using pre-trained Faster R-CNN residual network (ResNet)-50 and ResNet-101 model is utilized to detect and localise dental caries. The Faster R-CNN ResNet-50 model trained using the Adam optimizer produces a mean average precision (mAP) of 0.213, and those using the momentum optimizer produce a mAP of 0.177. While the Faster R-CNN ResNet-101 model trained using the Adam optimizer produces a mAP of 0.192, and those using the momentum optimizer produce a mAP of 0.004. The model trained on the dataset showed satisfactory results in detecting dental caries, especially ResNet-50 with Adam optimizer.

CaSnLi-YOLO: construction site multi-target detection method based on improved YOLOv5s

To address the problem of the frequent occurrence of major casualties during construction, a lightweight multi-target detection model based on YOLOv5s, named CaSnLi-you only look once (YOLO), was proposed for the detection of multiple targets consisting of construction workers and various mechanical equipment at construction sites. In this study, the introduction of the coordinate attention mechanism along with DWConv and C3Ghost based on GhostNet, enhanced the expression and detection accuracy of the model, effectively reducing the number of parameters. Considering the environmental characteristics of construction sites, a detection box filtering strategy based on soft non-maximum suppression was employed, to effectively alleviate the issue of missed detections of occluded targets. Experimental results demonstrate the significant superiority of the proposed CaSnLi-YOLO over current mainstream detection models, such as faster region-based convolutional neural network and single-shot detector. The proposed CaSnLi-YOLO has a parameter number of 5.96 × 106, which is 15.2% less than that of the original YOLOv5s model, further improving precision, recall rate, mAP@0.5, and mAP@0.5:0.95 by 0.6%, 0.6 %, 0.2%, and 2.3%, respectively. The improved YOLOv5s model proposed in this study achieved significant improvements in multi-target detection at construction sites. The model demonstrated effective enhancements while significantly reducing parameter count and is expected to be deployed in small-edge devices for real-time security monitoring at construction sites.

A comprehensive end-to-end computer vision framework for restoration and recognition of low-quality engineering drawings

The digitization of engineering drawings is crucial for efficient reuse, distribution, and archiving. Existing computer vision approaches for digitizing engineering drawings typically assume the input drawings have high quality. However, in reality, engineering drawings are often blurred and distorted due to improper scanning, storage, and transmission, which may jeopardize the effectiveness of existing approaches. This paper focuses on restoring and recognizing low-quality engineering drawings, where an end-to-end framework is proposed to improve the quality of the drawings and identify the graphical symbols on them. The framework uses K-means clustering to classify different engineering drawing patches into simple and complex texture patches based on their gray level co-occurrence matrix statistics. Computer vision operations and a modified Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) model are then used to improve the quality of the two types of patches, respectively. A modified Faster Region-based Convolutional Neural Network (Faster R-CNN) model is used to recognize the quality-enhanced graphical symbols. Additionally, a multi-stage task-driven collaborative learning strategy is proposed to train the modified ESRGAN and Faster R-CNN models to improve the resolution of engineering drawings in the direction that facilitates graphical symbol recognition, rather than human visual perception. A synthetic data generation method is also proposed to construct quality-degraded samples for training the framework. Experiments on real-world electrical diagrams show that the proposed framework achieves an accuracy of 98.98% and a recall of 99.33%, demonstrating its superiority over previous approaches. Moreover, the framework is integrated into a widely-used power system software application to showcase its practicality. The reference codes and data can be found at https://github.com/Lattle-y/AI-recognition-for-lq-ed.git Future work will focus on improving the generalizability of the proposed framework to different quality degradation scenarios and extrapolating the application to different engineering domains.

Design an Effective, Faster Region-Based Convolutional Neural Network with Optimization for the Human Tracking System

Nowadays, real-time Human Tracking System (HTS) is a crucial topic in computer vision and image processing with applications like robotic perception, scene understanding, video surveillance, image compression, medical image analysis, and augmented reality, among many others. In this paper, we design a Faster Region-based Convolutional Neural Network (FR-CNN) with Crow Search Optimization (FR-CNN-CSO) architecture to improve computational complexity and enhance the performance of HTS. The system is implemented in a Python environment with video input. Remove unnecessary data from the gathered datasets during preprocessing. Next, feature extraction is processed using Histograms of Oriented Gradients (HOG). Then update, the extracted features into a designed FR-CNN model for identifying and tracking a person using crow search fitness. The main goal of the developed approach is to attain accurate prediction results and improve the computational complexity by achieving less execution time. Finally, the experimental outcomes show the reliability of the designed system by other conventional techniques in terms of accuracy, precision, recall, F-measure, and execution time.

Speech based emotion recognition by using a faster region-based convolutional neural network

Speech based emotion recognition by using a faster region-based convolutional neural network

Infrared image target detection for substation electrical equipment based on improved faster region-based convolutional neural network algorithm.

Substation electrical equipment generates a massive number of infrared images during operation. However, the overall quality of the infrared images is low and it lacks image detail information. When using traditional target detection algorithms for detection, feature extraction poses great difficulties. Therefore, to address this problem, this paper proposes a target detection algorithm based on the improved faster region-based convolutional neural network (Faster R-CNN). It achieves the correct identification of different types of electrical equipment in infrared images. First, the algorithm improves the backbone network of Faster R-CNN for feature extraction. An InResNet structure is proposed to replace the residual block structure of the original ResNet-34 network, which enhances the richness of feature extraction. Second, the rectified linear unit activation function in the original feature extraction network is replaced by the exponential linear unit activation function, and group normalization is used instead of batch normalization as the network normalization method. Then, the dense connection structure is introduced into the ResNet-34 network, and the whole network is called residual dense connection network. Finally, the improved Faster R-CNN is compared to the original Faster R-CNN, a single-shot multibox detector, and you only look once v3 plus spatial pyramid pooling. The experimental results show that the improved algorithm has the highest mean average precision and average recall for most of the substation electrical equipment in infrared images. Moreover, from the confidence level of the detected electrical equipment and the accuracy of the prediction box, the improved Faster R-CNN has the best performance.

Applying Machine Learning to Construct a Printed Circuit Board Gold Finger Defect Detection System

Machine vision systems use industrial cameras’ digital sensors to collect images and use computers for image pre-processing, analysis, and the measurements of various features to make decisions. With increasing capacity and quality demands in the electronic industry, incoming quality control (IQC) standards are becoming more and more stringent. The industry’s incoming quality control is mainly based on manual sampling. Although it saves time and costs, the miss rate is still high. This study aimed to establish an automatic defect detection system that could quickly identify defects in the gold finger on printed circuit boards (PCBs) according to the manufacturer’s standard. In the general training iteration process of deep learning, parameters required for image processing and deductive reasoning operations are automatically updated. In this study, we discussed and compared the object detection networks of the YOLOv3 (You Only Look Once, Version 3) and Faster Region-Based Convolutional Neural Network (Faster R-CNN) algorithms. The results showed that the defect classification detection model, established based on the YOLOv3 network architecture, could identify defects with an accuracy of 95%. Therefore, the IQC sampling inspection was changed to a full inspection, and the surface mount technology (SMT) full inspection station was canceled to reduce the need for inspection personnel.

Iraqi Kurd or Arab Male Authenticity Detection Based on Facial Feature

As an inherent human characteristic, ethnicity plays a fundamental and critical role in biometric identification. On the other hand, the human face is the core of man’s identity, and facts such as age and race are often extrapolated automatically from the face. The objective is to utilize computer technologies to identify and categorize ethnic groups based on facial features. Convolutional neural networks (CNN), which can automatically identify underlying patterns from data, excel at learning image features and have shown state-of-the-art performance in several visual recognition challenges, such as ethnicity detection. Although the automated classification of traits such as age, gender, and ethnicity is a well-researched topic, Iraqi ethnic groupings have not yet been addressed. This study seeks to tackle the challenge of predicting the ethnicity of Iraqi male individuals based on their facial traits for the two largest ethnic groups, the Arabs, and the Kurds. Male Iraqi Kurds and Arabs were each represented by 260 image samples. The dataset underwent a diverse array of preprocessing and data enhancement techniques, including image resizing, isolation, gamma correction, and contrast stretching. Moreover, to augment the dataset and expand its diversity, various techniques such as brightness adjustment, rotation, horizontal flip, and grayscale augmentations were systematically applied, effectively increasing the overall number of images, and enriching the dataset for improved model performance. Face images of Kurds and Arabs were classified using the Faster region-based CNN (RCNN) approach of deep learning. Due to insufficient data in the dataset, we propose employing transfer learning to extract features using several pre-trained models. Specifically, we examined EfficientNetB4, ResNet-50, SqueezeNet, VGG16, and MobileNetV2, resulting in accuracies of 96.73%, 94.91%, 93.39%, 92.48%, and 90.32%, accompanied by corresponding precision values of 0.86, 0.81, 0.80, 0.70, and 0.69, respectively. It is essential to emphasize that the following inference speeds – VGG16 (4.5 ms), ResNet-50 (4.6 ms), SqueezeNet (3.8 ms), MobileNetV2 (3.7 ms), and EfficientNet-B4 (16 ms) – represent the computing times needed for each backbone. Moreover, to achieve a harmonious trade-off between precision and the time required for inference, we chose ResNet-50 as the foundational framework for our model aimed at classifying ethnicity. The study also acknowledges limitations such as the availability and diversity of the dataset. Nevertheless, despite these limitations, it provides valuable perspectives on the automated prediction of Iraqi male ethnicity through facial features, presenting potential applications in various domains. The findings contribute to the broader conversation surrounding biometric identification and ethnic categorization, underscoring the importance of ongoing research and heightened awareness of the inherent limitations associated with such studies.

Comparative Analysis of CNN and Different R-CNN based Model for Prediction of Alzheimer’s Disease

INTRODUCTION: Medical images still need to be examined by medical personnel, which is a prolonged and vulnerable progression. The dataset used included 4 classes of 6400 training and test MRI images each and was collected from Kaggle such as cognitively normal (CN), Mild Cognitive Impairment stage (MCI), moderate cognitive impairment (Moderate MCI), and Severe stage of cognitive impairment (AD).
 OBJECTIVES: There was a glaring underrepresentation of the Alzheimer Disease (AD) class. The accuracy and effectiveness of diagnoses can be improved with the use of neural network models.
 METHODS: In order to establish which CNN-based algorithm performed the multi-class categorization of the AD patient's brain MRI images most accurately. Thus, examine the effectiveness of the popular CNN-based algorithms like Convolutional Neural Network (CNN), Region-based CNN (R-CNN), Fast R-CNN, and Faster R-CNN.
 RESULTS: On the confusion matrix, R-CNN performed the best.
 CONCLUSION: R-CNN is quick and offers a high precision of 98.67% with a low erroneous measure of 0.0133, as shown in the research.

MBA-DNet: A mask block attention-based foreign matter detection network for tobacco packages.

Foreign matter, such as varia and mildew in the cutaway view of tobacco packages, can be detected using machine vision detection technology. However, mainstream object detection algorithms have poor detection ability for small targets when applied to foreign matter detection in the cutaway view of tobacco packages. To solve this problem, this study proposes Mask Block Attention (MBA) and introduces it into the feature extraction network to improve the global modeling ability of the object detection network, further enhancing its ability to detect foreign matter in the cutaway view of tobacco packages. Meanwhile, this study establishes a K-fold packet slicing defect dataset called K-PSDDS (K-fold packet slicing defect dataset) for foreign matter in the cutaway view of tobacco packages and conducts numerous experiments on K-PSDDS. The experimental results indicate that the AP50 and APbbox of DINO (DETR with an improved denoising anchor box for end-to-end target detection) + MBA reached 94.9% and 47.7%, respectively, showing an improvement of 0.3% and 0.9% over the baseline network DINO. Meanwhile, it achieves better performance and detection capabilities than fast region-based convolutional neural networks and other detection algorithms.

Backbone search for object detection for applications in intrusion warning systems

<span>In this work, we propose a novel backbone search method for object detection for applications in intrusion warning systems. The goal is to find a compact model for use in embedded thermal imaging cameras widely used in intrusion warning systems. The proposed method is based on faster region-based convolutional neural network (Faster R-CNN) because it can detect small objects. Inspired by EfficientNet, the sought-after backbone architecture is obtained by finding the most suitable width scale for the base backbone (ResNet50). The evaluation metrics are mean average precision (mAP), number of parameters, and number of multiply–accumulate operations (MACs). The experimental results showed that the proposed method is effective in building a lightweight neural network for the task of object detection. The obtained model can keep the predefined mAP while minimizing the number of parameters and computational resources. All experiments are executed elaborately on the person detection in intrusion warning systems (PDIWS) dataset. </span>

A Comparative Analysis of CNN, RCNN & Faster RCNN Object Detection Algorithm for CAPTCHA Breaking

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) systems serve as a crucial defense mechanism against automated attacks by distinguishing between human users and bots. However, advancements in deep learning have posed significant challenges to the security of conventional CAPTCHA systems. In this research paper, we present a comparative analysis of two prominent object detection algorithms, Convolutional Neural Networks (CNN) and Region-based Convolutional Neural Networks (RCNN), for breaking CAPTCHAs. The study evaluates the performance of CNN and RCNN algorithms in accurately identifying and deciphering characters within CAPTCHA images. Utilizing a diverse dataset of CAPTCHA samples, experiments are conducted to assess the effectiveness of both algorithms in handling variations in CAPTCHA styles, languages, and complexities. Through extensive experimentation and evaluation, we analyze the strengths and limitations of CNN and RCNN in the context of CAPTCHA breaking. Key metrics such as accuracy, precision, recall, and computational efficiency are compared to provide insights into the relative performance of each algorithm. The findings of this research contribute to the understanding of object detection techniques for CAPTCHA breaking and provide valuable insights for enhancing the security of CAPTCHA systems against emerging threats posed by deep learning-based attacks.

Precast concrete project image dataset for deep learning object detection

Computer vision technology in precast concrete (PC) projects has the potential to enhance site management but faces challenges due to the lack of specialized datasets for this field. This study developed a PC components dataset for object detection (PCCODA) in four steps: component range selection, image collection, image preprocessing, and labeling. The performance of PCCODA was assessed using multiple algorithms, including You Only Look Once (YOLO), YOLO X, faster region-based convolutional neural networks (Faster R-CNN), and Double Head R-CNN. The dataset resulted in average accuracies of 0.88–0.97, average robustness of 0.84–0.96, and average detection speed of 3.64–23.3 frames per second. This performance fulfills the level required by most studies on construction automation. The applicability of the dataset was tested at a real site. This study contributes to the off-site construction project management theory and enhances productivity in PC projects by automating object detection-related tasks.

Advancing disease identification in fava bean crops: A novel deep learning solution integrating YOLO-NAS for precise rust

A significant concern is the economic impact of agricultural diseases on the world’s crop production. The disease significantly reduces agricultural production across the world. Loss of nutrients caused by parasite infection of leaves, pods, and roots–the pathogenic agent that causes fava bean rust disease–decreases crop health. This work addresses this requirement by offering an innovative deep-learning model approach for early identification and classification of fava bean rust disease. The suggested method uses the effectiveness of modern YOLO-based object detection architectures like You Only Look Once –Neural Architecture Search (YOLO-NAS) L, YOLO-NASM, and YOLO-NASS, Faster Region-based Convolutional Neural Network (Faster R-CNN), and RetinaNet. An inclusive dataset of 3296 images of various lighting and background situations was selected for extensive model training. Each model underwent thorough training and adjusted parameters through careful experimentation. The models’ comparative studies found significant performance differences. The precision for YOLO-NASL was 82.10%; for YOLO-NASM, it was 84.80%; for YOLO-NASS, it was 83.90%; for Faster R-CNN, it was 75.51%; and for RetinaNet, it was 73.74%. According to the evaluation, model complexity and detection accuracy are directly correlated. YOLO-NASL, YOLO-NASM, and YOLO-NASS showed remarkable mean average precision values of 90.90%, 94.10%, and 92.60%, respectively, and became highly functional models. The fastest model was YOLO-NASS. Its satisfying recognition speed made real-time detection possible in particular applications. The YOLO-NASM model, which shows an extraordinary state-of-the-art performance, represents the pinnacle of our work. Its mean average precision (mAP@0.5) was 94.10%, with notable values of 90.84%, 96.96%, and 84.80% for the F1-score, Recall, and precision, respectively. This investigation addresses a critical need in agricultural disease management, aligning with broader global efforts toward sustainable agriculture. Our studies add to the knowledge about precision agriculture and inspire practical, long-lasting disease management techniques in the agricultural industry. The real-time performance of the system will need to be improved, and satellite imagery integration may be considered in the future to provide more comprehensive coverage.

Pest recognition in microstates state: an improvement of YOLOv7 based on Spatial and Channel Reconstruction Convolution for feature redundancy and vision transformer with Bi-Level Routing Attention.

In order to solve the problem of precise identification and counting of tea pests, this study has proposed a novel tea pest identification method based on improved YOLOv7 network. This method used MPDIoU to optimize the original loss function, which improved the convergence speed of the model and simplifies the calculation process. Replace part of the network structure of the original model using Spatial and Channel reconstruction Convolution to reduce redundant features, lower the complexity of the model, and reduce computational costs. The Vision Transformer with Bi-Level Routing Attention has been incorporated to enhance the flexibility of model calculation allocation and content perception. The experimental results revealed that the enhanced YOLOv7 model significantly boosted Precision, Recall, F1, and mAP by 5.68%, 5.14%, 5.41%, and 2.58% respectively, compared to the original YOLOv7. Furthermore, when compared to deep learning networks such as SSD, Faster Region-based Convolutional Neural Network (RCNN), and the original YOLOv7, this method proves to be superior while being externally validated. It exhibited a noticeable improvement in the FPS rates, with increments of 5.75 HZ, 34.42 HZ, and 25.44 HZ respectively. Moreover, the mAP for actual detection experiences significant enhancements, with respective increases of 2.49%, 12.26%, and 7.26%. Additionally, the parameter size is reduced by 1.39 G relative to the original model. The improved model can not only identify and count tea pests efficiently and accurately, but also has the characteristics of high recognition rate, low parameters and high detection speed. It is of great significance to achieve realize the intelligent and precise prevention and control of tea pests.

Vehicle detection and tracking in intelligent transportation systems based deep learning

This research paper focuses on the advancements and optimizations made to fundamental object detection algorithms in vehicle detection. The study explores integrating and reusing CNN (Convolutional Neural Networks) models with other techniques to enhance performance. Three main models, namely Faster R-CNN (Faster Region-based Convolutional Neural Network), Improved SSD (Single Shot Multibox Detector), and YOLOv4 (You Only Look Once v4), are analyzed, showcasing their incremental improvements in accuracy and overall detection performance. However, the increased computational complexity and time demands are trade-offs. The study also presents EnsembleNet, a model combining Faster R-CNN and YOLOv5, which achieves higher average precision values. Another approach involves fusing edge features with CNN models, resulting in faster and more accurate vehicle recognition. The paper predicts future deep learning trends, emphasizing the need for improved hardware capabilities to handle complex models. Integrating deep learning with sensor fusion and edge computing holds promise for intelligent transportation systems.

Robust individual pig tracking

The locations of pigs in the group housing enable activity monitoring and improve animal welfare. Vision-based methods for tracking individual pigs are noninvasive but have low tracking accuracy owing to long-term pig occlusion. In this study, we developed a vision-based method that accurately tracked individual pigs in group housing. We prepared and labeled datasets taken from an actual pig farm, trained a faster region-based convolutional neural network to recognize pigs’ bodies and heads, and tracked individual pigs across video frames. To quantify the tracking performance, we compared the proposed method with the global optimization (GO) method with the cost function and the simple online and real-time tracking (SORT) method on four additional test datasets that we prepared, labeled, and made publicly available. The predictive model detects pigs’ bodies accurately, with F1-scores of 0.75 to 1.00, on the four test datasets. The proposed method achieves the largest multi-object tracking accuracy (MOTA) values at 0.75, 0.98, and 1.00 for three test datasets. In the remaining dataset, the proposed method has the second-highest MOTA of 0.73. The proposed tracking method is robust to long-term occlusion, outperforms the competitive baselines in most datasets, and has practical utility in helping to track individual pigs accurately.

Optimization of table tennis target detection algorithm guided by multi-scale feature fusion of deep learning.

This paper aims to propose a table tennis target detection (TD) method based on deep learning (DL) and multi-scale feature fusion (MFF) to improve the detection accuracy of the ball in table tennis competition, optimize the training process of athletes, and improve the technical level. In this paper, DL technology is used to improve the accuracy of table tennis TD through MFF guidance. Initially, based on the FAST Region-based Convolutional Neural Network (FAST R-CNN), the TD is carried out in the table tennis match. Then, through the method of MFF guidance, different levels of feature information are fused, which improves the accuracy of TD. Through the experimental verification on the test set, it is found that the mean Average Precision (mAP) value of the target detection algorithm (TDA) proposed here reaches 87.3%, which is obviously superior to other TDAs and has higher robustness. The DL TDA combined with the proposed MFF can be applied to various detection fields and can help the application of TD in real life.

Maize tassel detection with CA-YOLO for UAV images in complex field environments

Maize detasseling is a key step in maize cross-pollination and is a key part of maize seed production, and the accurate identification and detection of maize tassels is a prerequisite for maize detasseling. The traditional manual method has the disadvantages of strong subjectivity, high workload, and poor timeliness. An artificial intelligence algorithm based on deep learning provides a new method for maize tassel detection. In this research, an uncrewed aerial vehicle (UAV) collected data for 10 consecutive days, covering the entire maize tasseling period. Data collected under different weather conditions and UAV flight altitudes were used to construct a diverse dataset for maize tassel detection. A preprocessing strategy suitable for the tassel dataset was developed through a dataset analysis. Then, a coordinate-attention(CA) mechanism was added to the backbone network of You Only Look Once version 5 to enhance the regression and localization capability of the model by embedding location information to extract important features. The improved model is called CA-YOLO. Comparative experiments revealed that CA-YOLO provides an effective method for detecting maize tassels, with an average precision of 96 %, surpassing the YOLO series models, such as YOLOv5m and YOLOv7, and classical detection models, such as the faster region-based convolutional neural network and single-shot detector. Moreover, CA-YOLO is robust and can effectively detect early-stage, leaf-obscured, mutually obscured, and complex-background tassels. This study provides a theoretical basis for accurately monitoring tassels and mechanical detasseling systems.

Multimodal deep learning model for Covid-19 detection

This study introduces a novel multimodal deep learning model for detecting Covid-19, uniquely combining chest X-ray images and cough sound features. We incorporated an attention layer to effectively influence the inputs from both features to enhance the detection process. The integration of time and frequency domain cough features with chest X-ray images represents a significant advancement in Covid-19 detection methodologies, offering promising results and potential applications in the medical field. In the proposed model, we modified vgg16 and a faster region-based convolutional neural network (FRCNN). The modified vgg16 is used to extract the features from both datasets, whereas the modified FRCNN is used for detection. Utilizing the Vgg16 model for feature extraction and faster-rcnn for Covid-19 detection allows for a robust and efficient approach. We analyzed the performance of the proposed methodology using a dataset of 2059 Covid-19 positive chest X-ray images and a diverse collection of 25,000 cough recordings, including 716 positive, 7693 negative, and 1591 symptomatic cases. The datasets were divided into 80% for training and 20% for testing. The model achieves 99.80% accuracy, an f1-score of 99.70%, and 0.0132 validation loss. We further performed an extensive ablation experiment on the features. We compared the results of the proposed model to already existing networks using chest X-rays and coughs as datasets, and the proposed model outperformed them. Experiments also show that the trained model resists training data quantity and quality and can effectively detect Covid-19 in chest X-rays and coughs.