Faster Region Convolutional Neural Network Research Articles

Breast Cancer Detection in Mammography using Faster Region Convolutional Neural Networks and Group Convolution

Breast Cancer Detection in Mammography using Faster Region Convolutional Neural Networks and Group Convolution

AI- POWERED BASED BLIND AND VISUALLY IMPAIRED SYSTEM FOR SMART GLASS

Visual impairment causes serious problems in daily life and mobility for millions of people worldwide. Against this background, smart assistive technology is gaining momentum to improve the independence and quality of life of the blind and visually impaired. They have difficulty in daily life because they cannot detect the problems around them, and one of their biggest problems is identifying people. Besides automation, there are many unexplored uses of search engines. This project includes an app that uses search to help blind people see things in front of them safely, and a facial recognition system with visual feedback that can help visually impaired people see familiar and unfamiliar people. The speaker will provide them with sound support. In this work, we use deep learning-based Regional Convolutional Neural Network (Faster R-CNN) to detect and identify people and objects in the environment. Faster local convolutional neural network technology processes and distributes images captured by cameras. The operator takes the image as input. Therefore, this model helps visually impaired people in a simpler way than a white cane. Keywords—Visuallmpairment,Objectdetection,Faster Region Convolutional Neural Network.

Defect Detection of Cell Phone Screen Using a Faster Regional Convolutional Neural Network with Multi-head Attention Mechanism

Since the glass outer screen of a cell phone is the main sensory part of the human eye when using a cell phone, the advantages and disadvantages of the cell phone screen directly affect people's sense of use. Therefore, the defect detection requirements for cell phone screens are high and need to meet the needs of high-volume factory inspection. Most of the traditional defect detection methods use visual methods, the detection results are overly dependent on the subjectivity and experience of workers, the efficiency of this method is low, and the accuracy is poor. Currently, machine learning-based detection methods are applied in numerous industries. In this paper, a faster Regional Convolutional Neural Network (R-CNN) with multi-head attention mechanism for defect detection of cell phone screen is proposed. To enhance the network's capability in extracting feature information, a four-head attention mechanism is added to the last convolutional layer of the ResNet50 network. An improved Region of Interest (ROI) Align is proposed to replace the original ROI Pooling to reduce the localization error of cell phone screen defects. Replace the original Rectified Linear Unit (ReLU) activation function with the Copy Exponential Linear Unit (CELU) activation function to expedite the convergence capability of the network. Finally, by comparing with other classical model training, the evaluation results indicate that the proposed method achieved an average accuracy of 95.71%, which is a 5.34% improvement compared to the original faster R-CNN network.

A deep learning model based on magnifying endoscopy with narrow-band imaging to evaluate intestinal metaplasia grading and OLGIM staging: A multicenter study

Background and PurposePatients with stage III or IV of operative link for gastric intestinal metaplasia assessment (OLGIM) are at a higher risk of gastric cancer (GC). We aimed to construct a deep learning (DL) model based on magnifying endoscopy with narrow-band imaging (ME-NBI) to evaluate OLGIM staging. MethodsThis study included 4473 ME-NBI images obtained from 803 patients at three endoscopy centres. The endoscopic expert marked intestinal metaplasia (IM) regions on endoscopic images of the target biopsy sites. Faster Region-Convolutional Neural Network model was used to grade IM lesions and predict OLGIM staging. ResultsThe diagnostic performance of the model for IM grading in internal and external validation sets, as measured by the area under the curve (AUC), was 0.872 and 0.803, respectively. The accuracy of this model in predicting the high-risk stage of OLGIM was 84.0%, which was not statistically different from that of three junior (71.3%, p = 0.148) and three senior endoscopists (75.3%, p = 0.317) specially trained in endoscopic images corresponding to pathological IM grade, but higher than that of three untrained junior endoscopists (64.0%, p = 0.023). ConclusionThis DL model can assist endoscopists in predicting OLGIM staging using ME-NBI without biopsy, thereby facilitating screening high-risk patients for GC.

Faster Region Convolutional Neural Network (FRCNN) Based Facial Emotion Recognition

Faster Region Convolutional Neural Network (FRCNN) Based Facial Emotion Recognition

Classification and Localization of Arabic Handwritten Text in KHATT Dataset Based on Faster R-CNN

Arabic handwriting recognition is an important research area in computer vision. Due to the complexity of the Arabic script, this is an arduous task. Several approaches have been proposed to address this challenge, including deep learning algorithms. Despite their efficiency, these approaches present some limitations such as the use of lexicon-driven models, the need of a lot of data for training and the huge computational cost. We propose, in this paper, two novel models based on the robust Faster Region-Convolution Neural Network (Faster R-CNN) to recognize Arabic handwritten sentences. The Faster R-CNN is commonly used to detect objects in images and depends on region proposal algorithms. These models use the pre-trained VGG 16 and ResNet50. Moreover, they use a soft non-maximum suppression strategy (Soft-NMS) in the post-processing stage instead of the traditional NMS to increase the detection rate of overlapping items. The models will be trained independently to identify words in the text lines and tested using sentences from the (KFUPM Handwritten Arabic TexT) KHATT dataset. It will be shown that the faster R-CNN models allow greater accuracy and effectiveness in handwriting recognition. They achieve accuracy rates of 99% and 98% for the two networks, VGG16 and ResNet50, respectively

Melanoma Diagnosis Using Enhanced Faster Region Convolutional Neural Networks Optimized by Artificial Gorilla Troops Algorithm

Melanoma, a rapidly spreading and perilous type of skin cancer, is the focus of this study, presenting a reliable technique for its detection. It is one of the most prevalent types of cancer that might be challenging for medical professionals to diagnose. Artificial intelligence can improve diagnostic accuracy when utilized in conjunction with the expertise of medical specialists. An innovative computer-aided method for the diagnosis of skin cancer has been introduced in the current study. The construction of the proposed method uses the African Gorilla Troops Optimizer (AGTO) Algorithm, a recently introduced meta-heuristic optimization algorithm, and deep learning models such as Faster Region Convolutional Neural Networks. To reduce the complexity of the analytic process, valuable features are chosen using the AGTO method, and further classification is implemented using Faster R-CNN. The proposed model is applied to the ISIC-2020 skin cancer dataset. When the final performance results from the proposed model are compared to those from four existing works, the findings show that the proposed system outperforms the existing models with an accuracy of 98.55%.

Image Object Detection Method Based on Improved Faster R-CNN

Aiming at the problems about the incompleteness and low accuracy in classifying image feature extraction in existing image object detection methods. First, an improved image object detection method based on faster Region-Convolutional Neural Network (RCNN) is proposed, and Region of Interest (ROI) align is used instead of RoI pooling to reduce the error in the pooling process. Second, it adds the layer of convolution before adding network full-connection layer, thus reducing its parameters, enhance the performance of the classifier and avoid over-fitting. Finally, the combination of softmax and central loss functions is for training network-based model to increase the differences between categories and reduce the changes within categories, and thus the model with network could have more targeted and diversified features. Research results found that in total, method average accuracy is 91.04%, indicating higher than the accuracy of other methods and has good image target detection ability.

Microscopic hyperspectral imaging and an improved detection model based detection of Mycogone perniciosa chlamydospore in soil

The Mycogone perniciosa disease of Agaricus bisporus is highly contagious, with insignificant early symptoms and a long infestation period. Currently, there is a lack of a convenient and rapid means to detect the disease for early control. By using the transmission route of the disease, this paper focuses on M. perniciosa chlamydospore detection in contaminated soil. Microscopic hyperspectral images of M. perniciosa chlamydospore in contaminated soil were obtained to establish a detection model. Given a small target and a complex background, we proposed an improved spore detection model based on a faster regional convolutional neural network (Faster R-CNN). Furthermore, we combined the residual network Resnet50 and feature pyramid network (FPN) to extract thick spore target features at multiple scales. Meanwhile, we optimized the region proposal network (RPN) region proposal generation by adding two small scales to improve the performance of the detection model. The first three principal components (with 95% or more information) and RGB images were selected as model inputs, respectively, and the final average precision (AP) was 94.68% and 92.35%, respectively. This PC-based model also was compared to the VGG16 and Resnet50-based feature extraction networks of Faster R-CNN and Darknet53-based feature extraction network of the YOLOv3 model, and the AP was found to improve by 5.41%, 4.78%, and 6.34%, respectively. The results showed that micro-hyperspectral imaging combined with deep learning methods could accurately detect the chlamydospore in the soil, providing new methods and ideas for the early prevention and detection of M. perniciosa disease of A. bisporus.

Online verification and management scheme of gateway meter flow in the power system by machine learning

Currently, the calibration of electric energy meters often involves manual meter reading, dismantling inspection, or regular sampling inspection conducted by professionals. To improve work efficiency and verification accuracy, this research integrates machine learning into the scheme of online verification and management of gateway meter flow in the power system. The approach begins by applying the Faster Region Convolutional Neural Network (Faster-RCNN) model and the Single Shot MultiBox Detector (SSD) model to the recognition system for dial readings. Then, the collected measurement data is pre-processed, excluding data collected under light load conditions. Next, an estimation error model and a solution equation for the electricity meter are established based on the pre-processed data. The operation error of the electricity meter is estimated, and the estimation accuracy is verified using the limited memory recursive least squares algorithm (LMRLSA). Furthermore, business assistant decision-making is carried out by combining the remote verification results with the estimation outcomes. The proposed dial reading recognition system is tested using 528 images of meter readings, achieving an accuracy of 98.49%. In addition, the influence of various parameters on the error results of the electricity meter is also explored. The results demonstrate that a memory length ranging from 600 to 1,200 and a line loss error of less than 5% yield the most suitable accuracy for estimating the electricity meter error. Meanwhile, it is advisable to remove measurement data collected under light load to avoid unnecessary checks. The experiments manifest that the proposed algorithm can properly eliminate the influence of old measurement data on the error parameter estimation, thereby enhancing the accuracy of the estimation. The adjustment of the memory length ensures real-time performance in estimating meter errors and enables online monitoring. This research has certain reference significance for achieving the online verification and management of gateway meter flow in the power system.

E-YOLO: Recognition of estrus cow based on improved YOLOv8n model

Timely and accurately identifying estrus cows is essential in modern dairy farming. To address the challenges of delayed and inefficient manual monitoring of cow estrus, an improved model based on You Only Look Once v8 Nano (YOLOv8n), named Estrus-YOLO (E-YOLO), was proposed to identify estrus cows efficiently. In this research, the dataset was labelled not only for cow mounting behavior but also innovatively labelled individual estrus cows, enabling precise identification of estrus cows. Due to the small size of cow in the field of view, the Complete Intersection over Union (CIoU) loss was replaced with the Normalized Wasserstein Distance (NWD) loss to reduce sensitivity to position deviations of target cows. Context Information Augmentation Module (CIAM) was proposed to enhance the contextual information for estrus cows by utilizing cow mounting behavior as reference features. Furthermore, the Triplet Attention Module (TAM) was incorporated into the Backbone to enhance the network’s focus on individual estrus cows through cross-dimensional interactions. To validate the effectiveness of the algorithm, experiments were conducted on a dataset consisting of 1716 instances of cow mounting behavior. The experimental results demonstrated that the proposed model achieved an Average Precision of estrus (APestrus) of 93.90%, Average Precision of mounting (APmounting) of 95.70%, F1-score of 93.74%, detection speed of 8.1 ms/frame, with the parameters of 3.04 M, and the Floating-point Operations (FLOPs) of 9.9 G. Compared to the YOLOv8 model, the proposed model exhibited an improvement of 5.40% in APestrus and 3.30% in APmounting. When compared to Single Shot MultiBox Detector (SSD), Faster Region Convolutional Neural Network (Faster R-CNN), YOLOv5n, YOLOv5s, YOLOv7-tiny, YOLOv8n, and YOLOv8s, the proposed model had fewer parameters, FLOPs, and fast detection speed. Except for APmounting, which was slightly lower than SSD, the rest accuracy indexes were the highest, showing good comprehensive performance and meeting the requirements of accurate and rapid identification of estrus cows. The proposed model was helpful for accurate and real-time monitoring of estrus cows in complex breeding environments and all-weather conditions.

Automatic spacing inspection of rebar spacers on reinforcement skeletons using vision-based deep learning and computational geometry

Improper spacing of rebar spacers (RSs) in reinforced concrete structures can lead to corrosion and damage of rebars, ultimately decreasing the strength and durability of the structure. The current method for RSs spacing inspection entails a manual inspection that utilizes a measuring tape, which is both labor-intensive and time-consuming. This paper presents a novel method for automatically inspecting RSs spacing using a vision-based deep learning and computational geometry. The proposed method consists of three modules including the RSs location and classification module, the keypoints detection module and RSs spacing inspection module. The RSs location and classification module employ object detection of faster-region convolutional neural network (Faster-RCNN)-based deep learning to accurately locate and classify RSs. The keypoints detection module uses human pose estimation of cascaded pyramid network (CPN)-based deep learning to detect the three keypoints necessary for accurate RSs spacing calculations. In RSs spacing inspection module, the computational geometry is applied to derive an accurate expression for calculating RSs spacing in the transverse and longitudinal directions, based on the RSs keypoints coordinates and camera parameters. The RSs spacing is subsequently evaluated to check its compliance. In the experiment, the algorithms of the three modules were integrated into a robot that can automatically move and take pictures to accomplish the task of automatically inspecting RSs spacing in a large reinforced concrete component plant. The experimental results indicate that this method yields an average RSs spacing measurement error of 1.35 cm. Finally, the robustness of the proposed method was validated by changing the conditions of image acquisition processes.

Human Identification and Obstacle Detection System for Blind

Abstract: According to the World Health Organization (WHO), there are millions of visually impaired individuals worldwide in detecting obstacles and identifying people. It emphasizes the advancements in information technology and spatial cognition theory for visually impaired individuals as a new opportunity. The prototype proposes a simple and cost-effective solution using artificial vision through an AI-based intelligent system. The system utilizes the Faster Region Convolutional Neural Network (FRCNN) architecture to recognize human and scene objects or obstacles in real-time, even in complex environments. It provides users with comprehensive information about the presence, position, and nature of targets, and uses voice messages to alert blind individuals about obstacles or people nearby. The goal is to create a user-friendly technology that facilitates communication and independence for visually impaired individuals, enabling them to navigate both indoor and outdoor locations effectively.

Research on Insect Pest Identification in Rice Canopy Based on GA-Mask R-CNN

Aiming at difficult image acquisition and low recognition accuracy of two rice canopy pests, rice stem borer and rice leaf roller, we constructed a GA-Mask R-CNN (Generative Adversarial Based Mask Region Convolutional Neural Network) intelligent recognition model for rice stem borer and rice leaf roller, and we combined it with field monitoring equipment for them. Firstly, based on the biological habits of rice canopy pests, a variety of rice pest collection methods were used to obtain the images of rice stem borer and rice leaf roller pests. Based on different segmentation algorithms, the rice pest images were segmented to extract single pest samples. Secondly, the bug generator based on a generative adversarial network strategy improves the sensitivity of the classification network to the bug information, generates the pest information images in the real environment, and obtains the sample dataset for deep learning through multi-way augmentation. Then, through adding channel attention ECA module in Mask R-CNN and improving the connection of residual blocks in the backbone network ResNet101, the recognition accuracy of the model is improved. Finally, the GA-Mask R-CNN model was tested on a multi-source dataset with an average precision (AP) of 92.71%, recall (R) of 89.28% and a balanced score F1 of 90.96%. The average precision, recall, and balanced score F1 are improved by 7.07, 7.65, and 8.83%, respectively, compared to the original Mask R-CNN. The results show that the GA-Mask R-CNN model performance indexes are all better than the Mask R-CNN, the Faster R-CNN, the SSD, the YOLOv5, and other network models, which can provide technical support for remote intelligent monitoring of rice pests.

A Tiny Object Detection Approach for Maize Cleaning Operations.

Real-time and accurate awareness of the grain situation proves beneficial for making targeted and dynamic adjustments to cleaning parameters and strategies, leading to efficient and effective removal of impurities with minimal losses. In this study, harvested maize was employed as the raw material, and a specialized object detection network focused on impurity-containing maize images was developed to determine the types and distribution of impurities during the cleaning operations. On the basis of the classic contribution Faster Region Convolutional Neural Network, EfficientNetB7 was introduced as the backbone of the feature learning network and a cross-stage feature integration mechanism was embedded to obtain the global features that contained multi-scale mappings. The spatial information and semantic descriptions of feature matrices from different hierarchies could be fused through continuous convolution and upsampling operations. At the same time, taking into account the geometric properties of the objects to be detected and combining the images' resolution, the adaptive region proposal network (ARPN) was designed and utilized to generate candidate boxes with appropriate sizes for the detectors, which was beneficial to the capture and localization of tiny objects. The effectiveness of the proposed tiny object detection model and each improved component were validated through ablation experiments on the constructed RGB impurity-containing image datasets.

An optimized modified faster region convolutional neural network for spina bifida identification from ultrasound images

Detecting spina bifida defects in an early parental stage and providing proper remedial measures is the main objective. There it demands effective learning approaches to automatically detect and classify fetal disability. However, the existing approaches face several complicated issues like time complexity, cost, and misclassification.This paper proposes a novel Modified Faster Region Convolutional neural Network based Cheetah Optimizer (Modified FRCNN-CO) model to accurately predict whether the child is defective with spina bifida disease or not.The ultrasound scanning images of the fetus acquired during the second trimester are considered an efficient screening tool for detecting fetus abnormalities. The ultrasound scan image of the fetal spine obtained during the 18thweek of pregnancy is taken as input. The poor quality and noise factors present in ultrasound images are enhanced and removed respectively using preprocessing pipelines namely contrast enhancement, intensity adjustment, and denoising. The enhanced images are segmented through the generative adversarial network (GAN) model. With the capability to capture data distribution, the GAN model segments and emphasizes defective regions of images. This procedure makes the proposed modified FRCNN-CO model accurately identify the images with spina bifida disease. Based on the features extracted, the proposed modified FRCNN-CO model accurately classifies and labels the ultrasound images into two classes normal ‘0′ and defective ‘1′.The experimental outcomes illustrate the supremacy of the proposed modified FRCNN-CO model over other compared methods, especially achieved detection accuracy of about 97.8%.

TW3-based ROI detection and classification using a chaotic ANN and DNN-EVGO architecture for an automated bone age assessment on hand X-ray images

ABSTRACT Bone illnesses arise at a young age itself, so bone age assessment (BAA) is primarily utilized in paediatrics to identify their growth. Several BAA-related methods are employed to determine bone maturity; however, they do not give accuracy and the rate of error increases. To overcome this issue, in this manuscript, TW3-based Region of Interest (ROI) identification and classification with Chaotic ANN (CANN), as well as Deep Neural Network optimized with Evolved Gradient Direction optimizer (EVGO) architecture, is proposed for an Automated BAA classification. In this, Hand X-Ray images are taken from the Digital Hand Atlas (DHA) dataset and the Radiological Society of North America (RSNA) database. Here, Tanner-Whitehouse (TW3) and Shallow convolution neural network (SCNN) is utilized for extracting the ROI regions such as radius, ulna, and short bones (RUS) from the wrist region of the left hand. After that, the extracted ROI feature data are given to CANN for predicting BAA. Then, the predicted data are classified using the DNN-EVGO network. The bone age evaluation is then classified depending on maturity phases. The proposed approach is activated in MATLAB. Simulation outcomes of TW3- SCNN- CANN- DNN-EVGO- BAA methods for DHA database attain higher accuracy of 45.75%, 37.64%, 24.64% when analysed to the existing models, such as Faster Region-Convolutional Neural network oriented feature learning with optimal trained Recurrent Neural Network for BAA for paediatrics (TW3-F-R-CNN-AF-SFO), Multi-objective segmentation approach for BAA under parameter tuning- U-net architecture, and TW3-based fully automated bone age assessment system using deep neural networks (TW3-F-R-CNN-VGGNet-CNN-BAA).

Enhancing Object Detection in Self-Driving Cars Using a Hybrid Approach

Recent advancements in artificial intelligence (AI) have greatly improved the object detection capabilities of autonomous vehicles, especially using convolutional neural networks (CNNs). However, achieving high levels of accuracy and speed simultaneously in vehicular environments remains a challenge. Therefore, this paper proposes a hybrid approach that incorporates the features of two state-of-the-art object detection models: You Only Look Once (YOLO) and Faster Region CNN (Faster R-CNN). The proposed hybrid approach combines the detection and boundary box selection capabilities of YOLO with the region of interest (RoI) pooling from Faster R-CNN, resulting in improved segmentation and classification accuracy. Furthermore, we skip the Region Proposal Network (RPN) from the Faster R-CNN architecture to optimize processing time. The hybrid model is trained on a local dataset of 10,000 labeled traffic images collected during driving scenarios, further enhancing its accuracy. The results demonstrate that our proposed hybrid approach outperforms existing state-of-the-art models, providing both high accuracy and practical real-time object detection for autonomous vehicles. It is observed that the proposed hybrid model achieves a significant increase in accuracy, with improvements ranging from 5 to 7 percent compared to the standalone YOLO models. The findings of this research have practical implications for the integration of AI technologies in autonomous driving systems.

Faster RCNN Target Detection Algorithm Integrating CBAM and FPN

In the process of image shooting, due to the influence of angle, distance, complex scenes, illumination intensity, and other factors, small targets and occluded targets will inevitably appear in the image. These targets have few effective pixels, few features, and no obvious features, which makes it difficult to extract their effective features and easily leads to false detection, missed detection, and repeated detection, thus affecting the performance of target detection models. To solve this problem, an improved faster region convolutional neural network (RCNN) algorithm integrating the convolutional block attention module (CBAM) and feature pyramid network (FPN) (CF-RCNN) is proposed to improve the detection and recognition accuracy of small-sized, occluded, or truncated objects in complex scenes. Firstly, it incorporates the CBAM attention mechanism in the feature extraction network in combination with the information filtered by spatial and channel attention modules, focusing on local efficient information of the feature image, which improves the detection ability in the face of obscured or truncated objects. Secondly, it introduces the FPN feature pyramid structure, and links high-level and bottom-level feature data to obtain high-resolution and strong semantic data to enhance the detection effect for small-sized objects. Finally, it optimizes non-maximum suppression (NMS) to compensate for the shortcomings of conventional NMS that mistakenly eliminates overlapping detection frames. The experimental results show that the mean average precision (MAP) of target detection of the improved algorithm on PASCAL VOC2012 public datasets is improved to 76.2%, which is 13.9 percentage points higher than those of the commonly used Faster RCNN and other algorithms. It is better than the commonly used small-sample target detection algorithm.

Deep Learning-Based Image Classification for Major Mosquito Species Inhabiting Korea.

Mosquitoes are one of the deadliest insects, causing harm to humans worldwide. Preemptive prevention and forecasting are important to prevent mosquito-borne diseases. However, current mosquito identification is mostly conducted manually, which consumes time, wastes labor, and causes human error. In this study, we developed an automatic image analysis method to identify mosquito species using a deep learning-based object detection technique. Color and fluorescence images of live mosquitoes were acquired using a mosquito capture device and were used to develop a deep learning-based object detection model. Among the deep learning-based object identification models, the combination of a swine transformer and a faster region-convolutional neural network model demonstrated the best performance, with a 91.7% F1-score. This indicates that the proposed automatic identification method can be rapidly applied for efficient analysis of species and populations of vector-borne mosquitoes with reduced labor in the field.