Faster R-CNN Research Articles

Unleashing hidden canines: a novel fast R-CNN based technique for automatic auxiliary canine impaction

Unleashing hidden canines: a novel fast R-CNN based technique for automatic auxiliary canine impaction

Real-time evaluation of object detection models across open world scenarios

Object detection models have been experiencing significant improvements over the years due to advancements in deep learning techniques, increased availability of large-scale annotated datasets, and computational resources. Different object detection models have varying levels of accuracy, speed, and robustness. With the increasing complexity and diversity of object detection models, it becomes a problem for researchers and practitioners to choose the most suitable model for their specific needs. This research paper outlines the escalating demand for robust comparison of object detection models in response to rapidly advancing technology. This evaluation helps in identifying the strengths and weaknesses of these models and selecting the most suitable one for a specific task. This highlights a significant challenge stemming from the lack of recent comparative studies on object detection models across various image qualities, object sizes, and training data sizes. The above challenges are tackled by a meticulous evaluation of three state-of-the-art object detection models: YOLO-v8, Faster R-CNN with ResNet 50 and 101 backbones, and End-to-End Object Detection Transformers (DETR) utilizing ResNet 50 and 101 backbones by employing a rigorous assessment framework encompassing mean Average Precision (mAP), accuracy, and inference speed. This study focuses on thoroughly examining how well the models perform across three different datasets: TACO, PlastOPol, and TACO 4.5. These datasets consist of open-world images captured in real-time from various locations. They include 1500, 2500, and 6500 images respectively, depicting real-world environments with varying lighting conditions and complex backgrounds. The results identify YOLOv8 as the superior model for high and medium-quality images, while Faster R-CNN performs better for low-quality images. However, DETR's accuracy falls short compared to other models. The paper fills a crucial gap in understanding model performance across varying image qualities and object sizes and helps in taking informed decisions in object detection systems.

AC-YOLO: Multi-category and high-precision detection model for stored grain pests based on integrated multiple attention mechanisms

The existing detection models for stored grain pests have low accuracy and poor generalization ability in fine-grained detection tasks involving numerous species, minor inter-class differences, and significant intra-class variations. This study addresses these issues by constructing a dataset comprising 3227 images of 12 species of grain pests and proposing an enhanced YOLOv7 algorithm for grain pest detection. This algorithm integrates the Convolutional Block Attention Module (CBAM) into the backbone network to enhance the perception of crucial features, thereby improving detection accuracy. Furthermore, the algorithm’s neck network incorporates the convolutional self-attention mechanism, ACmix, which improves algorithm efficiency and target sensitivity, reducing the rate of false negatives. The Efficient Complete Intersection over Union (ECIoU) is employed as the detection box loss function to augment the model’s generalization capabilities. Experimental results demonstrate that employing the proposed algorithm for detecting the 12 grain pests yields an mAP@0.5 and F1-score of 91.9% and 89.6%, respectively. Compared to the original algorithm, this represents an improvement of 6.2 and 3.4 percentage points, and the detection speed remains nearly constant. Moreover, the comprehensive performance of this algorithm surpasses that of Faster RCNN, SSD, other YOLO series, and the Deformable-detr algorithm. This method facilitates high-precision, real-time detection under conditions involving small targets and complex backgrounds, presenting a viable approach for developing intelligent grain pest detection systems.

Improved faster region convolutional neural network algorithm for UAV target detection in complex environment

With the development of artificial intelligence technology and Unmanned Aerial Vehicle (UAV) technology, the traditional UAV target detection methods are difficult to achieve high detection accuracy in complex and changeable scenes. The paper selects Faster Region Convolutional Neural Network (Faster R-CNN) as the basic algorithm due to its high scalability and excellent classification performance. The convolution mode of the convolution layer in the network structure is adjusted to deformable convolution. Additionally, a hybrid attention mechanism module is added to combine channel attention and spatial attention modules behind the output layer of the Faster R-CNN network. Finally, the Soft Non-Maximum Suppression (Soft-NMS) method is selected to combine the improved Faster R-CNN algorithm of multiple individuals into the final target detection model. The performance of the improved Faster R-CNN algorithm and the original Faster R-CNN algorithm was verified through the VisDrone 2018-DET dataset and the full class average accuracy Mean Average Precision (mAP), accuracy and precision. The accuracy and logarithm of loss values of the improved Faster R-CNN algorithm and the original Faster R-CNN algorithm's Region Proposal Network (RPN) were 0.985, 0.981, 0.018, and 0.052, respectively. The target detection model based on the hybrid attention mechanism of ResNet-50 network fusion Spartial Attention Module (SAM) and Channel Attention Module (CAM) had the best classification performance, with the accuracy, precision and overall average accuracy of 25.8 %, 24.6 % and 22.7 %, respectively. The results are helpful to improve the target detection ability of UAV in complex environment, and contribute to the development of target detection technology in the future.

Enhancing Seed Germination Test Classification for Pole Sitao (Vigna unguiculata (L.) Walp.) Using SSD MobileNet and Faster R-CNN Models

The classification of germinated pole sitao (Vigna unguiculata (L.) Walp.) seeds is important in seed germination tests. The automation of this process has been explored for different grain and legume seeds but is only limited to binary classification. This study aimed to develop a classifier system that can recognize three classes: normal, abnormal, and ungerminated. SSD MobileNet and Faster R-CNN models were trained to perform the classification. Both were trained using 1500 images of germinated seeds at fifth- and eighth-day observations. Each class had 500 images. The trained models were evaluated using 150 images per class. The SSD MobileNet model had an accuracy of 0.79 while the Faster R-CNN model had an accuracy of 0.75. The results showed that the average accuracies for the classes were significantly different from one another based on one-way ANOVA at a 95% confidence level with an F-critical value of 3.0159. The SSD MobileNet model outperformed the Faster R-CNN model in classifying pole sitao seeds, with improved precision in identifying abnormal and ungerminated seeds on the fifth day and normal and ungerminated seeds on the eighth day. The results confirm the potential of the SSD MobileNet model as a more reliable classifier in germination tests.

Oral Cancer Detection Using Deep Learning

Oral cancer is a very serious, complex, and common type of cancer. Oral cancer ranks eighth globally in terms of cancer incidence in India, with 130,000 deaths reported annually. The tumor affects the tonsils, salivary glands, neck, face, and mouth. Numerous methods, including screening procedures and biopsies—which entail taking a small sample of body tissue and analyzing it under a microscope—can be used to identify oral cancer. The disadvantage of cancer cells is that they are hard to identify and quantify. For this reason, digital processing technology will be employed in this study to identify and classify cancer cells that have affected the oral cavity. State-of-the-art technology and an in-depth learning algorithm can be employed for early detection and categorization. This work employs the Zernike Moment, wavelet features, and the bag histogram of directed gradients as three techniques for character extraction. After the characteristics are obtained, the best texture characteristic is chosen using the fuzzy particle swarm optimization technique (FPSO). In the end, these features were classified using the Faster Region-based Convolution Neural Network (faster RCNN) classifier. to evaluate the efficiency, error, recall rate, precision rate, and classification accuracy of the recommended approach.

3D residual attention hierarchical fusion for real-time detection of the prostate capsule

BackgroundFor prostate electrosurgery, where real-time surveillance screens are relied upon for operations, manual identification of the prostate capsule remains the primary method. With the need for rapid and accurate detection becoming increasingly urgent, we set out to develop a deep learning approach for detecting the prostate capsule using endoscopic optical images.MethodsOur method involves utilizing the Simple, Parameter-Free Attention Module(SimAM) residual attention fusion module to enhance the extraction of texture and detail information, enabling better feature extraction capabilities. This enhanced detail information is then hierarchically transferred from lower to higher levels to aid in the extraction of semantic information. By employing a forward feature-by-feature hierarchical fusion network based on the 3D residual attention mechanism, we have proposed an improved single-shot multibox detector model.ResultsOur proposed model achieves a detection precision of 83.12% and a speed of 0.014 ms on NVIDIA RTX 2060, demonstrating its effectiveness in rapid detection. Furthermore, when compared to various existing methods including Faster Region-based Convolutional Neural Network (Faster R-CNN), Single Shot Multibox Detector (SSD), EfficientDet and others, our method Attention based Feature Fusion Single Shot Multibox Detector (AFFSSD) stands out with the highest mean Average Precision (mAP) and faster speed, ranking only below You Only Look Once version 7 (YOLOv7).ConclusionsThis network excels in extracting regional features from images while retaining the spatial structure, facilitating the rapid detection of medical images.

Faster R-CNN based on frame difference and spatiotemporal context for vehicle detection

Faster R-CNN based on frame difference and spatiotemporal context for vehicle detection

Research on Intelligent Recognition for Plant Pests and Diseases Based on Improved YOLOv8 Model

Plant pests and diseases are important parts of insect disease control and the high-quality development of agriculture. Traditional methods for identifying plant diseases and pests suffer from low accuracy and slow speed, while the existing machine learning methods are constrained by environmental and technological factors, leading to low recognition efficiency. To address the issue of the above problems, this paper has proposed an intelligent recognition algorithm based on the improved YOLOv8 model, which has high recognition accuracy and speed. Firstly, in the Backbone network, the Global Attention Mechanism (GAM) is adopted to weigh the important feature information, thereby improving the accuracy of the model. Secondly, in the mixed feature part of the Neck network, the Receptive-Field Attention Convolutional (RFA Conv) operation is used instead of standard convolution operations to enhance the processing ability for feature information and to reduce computational complexity and costs, thus improving the network performance. After verifying the rice and cotton datasets, the accuracy indicator mean average precision (mAP) reaches 71.27% and 82.91%, respectively, in the two different datasets. Comparing these indices with those of the Faster R-CNN, YOLOv7, and the original YOLOv8 model, the results fully demonstrate the effectiveness and superiority of the improved model in terms of detection accuracy.

CRGF-YOLO: An Optimized Multi-Scale Feature Fusion Model Based on YOLOv5 for Detection of Steel Surface Defects

The identification of imperfections on steel surfaces is vital for ensuring the quality of industrial products. It requires the capability of real-time detection with high accuracy. This paper proposes the CRGF-YOLO (Contextual Reparameterized Generalized Feature) model based on YOLOv5. In the network, BottleneckCSP structures and depthwise separable convolutions utilizing the structural reparameterization are introduced to reduce the model size and improve performance. In addition, contextual transformer modules are employed as self-attention mechanisms to improve feature representations by capturing long-range dependencies, outperforming conventional convolutional networks. Furthermore, the simplified generalized feature pyramid network is embedded to aggregate multi-scale feature maps and enhance the network’s robustness. Finally, four prediction heads with different sizes are employed to predict defects, which are supported by prior bounding boxes generated using k-means clustering algorithm. The Focal-EIOU (Exponential Intersection over Union) loss function is introduced to improve detection accuracy and expedite model convergence. The improved model achieves a mean average precision (mAP) of 82.2% on the NEU-DET dataset, outperforming the baseline YOLOv5s by 7.7% mAP while maintaining real-time speeds. Comparative evaluations demonstrate CRGF-YOLO’s superior performance over previous state-of-the-art methods like Faster R-CNN (77.4% mAP), YOLOv3 (77.4% mAP), YOLOv7s (72.1% mAP), and YOLOv8s (78.7% mAP) for steel surface defect detection. Overall, this study provides valuable insights and practical guidance for the advancement of defect detection technology.

DeepLeish: a deep learning based support system for the detection of Leishmaniasis parasite from Giemsa-stained microscope images

BackgroundLeishmaniasis is a vector-born neglected parasitic disease belonging to the genus Leishmania. Out of the 30 Leishmania species, 21 species cause human infection that affect the skin and the internal organs. Around, 700,000 to 1,000,000 of the newly infected cases and 26,000 to 65,000 deaths are reported worldwide annually. The disease exhibits three clinical presentations, namely, the cutaneous, muco-cutaneous and visceral Leishmaniasis which affects the skin, mucosal membrane and the internal organs, respectively. The relapsing behavior of the disease limits its diagnosis and treatment efficiency. The common diagnostic approaches follow subjective, error-prone, repetitive processes. Despite, an ever pressing need for an accurate detection of Leishmaniasis, the research conducted so far is scarce. In this regard, the main aim of the current research is to develop an artificial intelligence based detection tool for the Leishmaniasis from the Geimsa-stained microscopic images using deep learning method.MethodsStained microscopic images were acquired locally and labeled by experts. The images were augmented using different methods to prevent overfitting and improve the generalizability of the system. Fine-tuned Faster RCNN, SSD, and YOLOV5 models were used for object detection. Mean average precision (MAP), precision, and Recall were calculated to evaluate and compare the performance of the models.ResultsThe fine-tuned YOLOV5 outperformed the other models such as Faster RCNN and SSD, with the MAP scores, of 73%, 54% and 57%, respectively.ConclusionThe currently developed YOLOV5 model can be tested in the clinics to assist the laboratorists in diagnosing Leishmaniasis from the microscopic images. Particularly, in low-resourced healthcare facilities, with fewer qualified medical professionals or hematologists, our AI support system can assist in reducing the diagnosing time, workload, and misdiagnosis. Furthermore, the dataset collected by us will be shared with other researchers who seek to improve upon the detection system of the parasite. The current model detects the parasites even in the presence of the monocyte cells, but sometimes, the accuracy decreases due to the differences in the sizes of the parasite cells alongside the blood cells. The incorporation of cascaded networks in future and the quantification of the parasite load, shall overcome the limitations of the currently developed system.

Fractional B-Spline Wavelets and U-Net Architecture for Robust and Reliable Vehicle Detection in Snowy Conditions.

This paper addresses the critical need for advanced real-time vehicle detection methodologies in Vehicle Intelligence Systems (VIS), especially in the context of using Unmanned Aerial Vehicles (UAVs) for data acquisition in severe weather conditions, such as heavy snowfall typical of the Nordic region. Traditional vehicle detection techniques, which often rely on custom-engineered features and deterministic algorithms, fall short in adapting to diverse environmental challenges, leading to a demand for more precise and sophisticated methods. The limitations of current architectures, particularly when deployed in real-time on edge devices with restricted computational capabilities, are highlighted as significant hurdles in the development of efficient vehicle detection systems. To bridge this gap, our research focuses on the formulation of an innovative approach that combines the fractional B-spline wavelet transform with a tailored U-Net architecture, operational on a Raspberry Pi 4. This method aims to enhance vehicle detection and localization by leveraging the unique attributes of the NVD dataset, which comprises drone-captured imagery under the harsh winter conditions of northern Sweden. The dataset, featuring 8450 annotated frames with 26,313 vehicles, serves as the foundation for evaluating the proposed technique. The comparative analysis of the proposed method against state-of-the-art detectors, such as YOLO and Faster RCNN, in both accuracy and efficiency on constrained devices, emphasizes the capability of our method to balance the trade-off between speed and accuracy, thereby broadening its utility across various domains.

Winter wheat ear counting based on improved YOLOv7x and Kalman filter tracking algorithm with video streaming.

Accurate and real-time field wheat ear counting is of great significance for wheat yield prediction, genetic breeding and optimized planting management. In order to realize wheat ear detection and counting under the large-resolution Unmanned Aerial Vehicle (UAV) video, Space to depth (SPD) module was added to the deep learning model YOLOv7x. The Normalized Gaussian Wasserstein Distance (NWD) Loss function is designed to create a new detection model YOLOv7xSPD. The precision, recall, F1 score and AP of the model on the test set are 95.85%, 94.71%, 95.28%, and 94.99%, respectively. The AP value is 1.67% higher than that of YOLOv7x, and 10.41%, 39.32%, 2.96%, and 0.22% higher than that of Faster RCNN, SSD, YOLOv5s, and YOLOv7. YOLOv7xSPD is combined with the Kalman filter tracking and the Hungarian matching algorithm to establish a wheat ear counting model with the video flow, called YOLOv7xSPD Counter, which can realize real-time counting of wheat ears in the field. In the video with a resolution of 3840×2160, the detection frame rate of YOLOv7xSPD Counter is about 5.5FPS. The counting results are highly correlated with the ground truth number (R2=0.99), and can provide model basis for wheat yield prediction, genetic breeding and optimized planting management.

Computer Vision for E-learning Student Face Reaction Detection for Improving Learning Rate

In the advent of e-learning, understanding student engagement and reaction is crucial for improving the quality of education and enhancing the learning rate. With the advancement of computer vision technologies, there is a significant opportunity to analyze and interpret student reactions in a non-intrusive manner. This study proposes a novel framework employing Faster R-CNN integrated with DenseNet architecture for real-time detection of student facial reactions during e-learning sessions. The proposed method leverages the strengths of Faster R-CNN in generating high-quality region proposals for object detection tasks, coupled with the DenseNet’s efficiency in feature propagation and reduction in the number of parameters, which is well-suited for processing the intricate patterns in facial expressions. Our approach begins with the application of Faster R-CNN to extract potential facial regions with high accuracy and reduced computational cost. The integration of DenseNet as a backbone for feature extraction within Faster R-CNN capitalizes on its densely connected convolutional networks, ensuring maximum information flow between layers in the network. By doing so, the system becomes exceptionally adept at recognizing subtle changes in facial features that indicate various student reactions, such as confusion, engagement, or boredom. We conducted a series of experiments using a diverse dataset of e-learning interactions, collected under various lighting conditions and involving multiple ethnicities to ensure robustness and generalizability. The model was trained and validated on this dataset, and the results demonstrate a significant improvement in detection rates of student reactions compared to existing methods.

Application of CNN based Regognition Technology in Public English Teaching in College Intelligent Classroom

With the advancement of intelligent technology, CNN-based recognition technology has been integrated into public English teaching in universities. This implementation contributes to enhancing teaching quality, nurturing students’ English proficiency, and holds significant educational and practical value. To address the issue of low traditional attendance efficiency in intelligent classrooms for public English teaching, a face recognition model based on CNN recognition technology has been developed. R-CNN is utilized for object detection, along with pyramid pooling and non-maximum suppression to acquire the optimal candidate region for face detection. Furthermore, K-Means clustering is combined to enhance Fast R-CNN, thereby improving detection accuracy. Experimental results demonstrated that among the three networks - Fast R-CNN, Faster R-CNN, and CNN -Faster R-CNN maintained a high recognition rate and exhibited faster convergence speed, showcasing superior overall performance. Specifically, at 500 iterations, the three networks require 23.7 seconds, 26.8 seconds, and 34.2 seconds, respectively. For facial expression recognition, Faster R-CNN achieved the highest recognition rate, indicating its exceptional detection efficiency and potential for aiding teaching management. This study offers novel technical support for public English teaching in intelligent university classrooms, effectively enhancing teaching efficacy and learning experiences. Its practical significance extends to promoting educational reform and improvement.

Research on Detection Technology of Micro- Components on Circuit Board Based on Digital Image Processing

Aiming at the stability of the circuit board image in the acquisition process, this paper realizes the accurate registration of the image to be registered and the standard image based on the SIFT feature operator and RANSAC algorithm. The device detection model and data set are established based on Faster RCNN. Finally, the number of training was continuously optimized, and when the loss function of Faster RCNN converged, the identification result of the device was obtained.

Comparing the accuracy of two machine learning models in detection and classification of periapical lesions using periapical radiographs.

Previous deep learning-based studies were mainly conducted on detecting periapical lesions; limited information in classification, such as the periapical index (PAI) scoring system, is available. The study aimed to apply two deep learning models, Faster R-CNN and YOLOv4, in detecting and classifying periapical lesions using the PAI score from periapical radiographs (PR) in three different regions of the dental arch: anterior teeth, premolars, and molars. Out of 2658 PR selected for the study, 2122 PR were used for training, 268 PR were used for validation and 268 PR were used for testing. The diagnosis made by experienced dentists was used as the reference diagnosis. The Faster R-CNN and YOLOv4 models obtained great sensitivity, specificity, accuracy, and precision for detecting periapical lesions. No clear difference in the performance of both models among these three regions was found. The true prediction of Faster R-CNN was 89%, 83.01% and 91.84% for PAI 3, PAI 4 and PAI 5 lesions, respectively. The corresponding values of YOLOv4 were 68.06%, 50.94%, and 65.31%. Our study demonstrated the potential of YOLOv4 and Faster R-CNN models for detecting and classifying periapical lesions based on the PAI scoring system using periapical radiographs.

Deep Learning Approaches for Water-body Detection from Satellite Imagery

Agriculture department of Government of India provides funds for digging wells and making farm ponds for improving farmers livelihood. The proper execution of the policies not been worked out due to the manual process. For the automation of the process, we are using the object detection algorithm Faster Region-based Convolutional Neural Network (F-RCNN) to detect the location of the wells and Farm ponds. The faster RCNN method introduced region of interest to improve the model speed. We have tried to address water body detection on satellite imagery with an AI approach to cross verify the utilization of funds is the challenge. The traditional object detection algorithms are low in accuracy, also uses coco image data set. Detecting water body has not been addressed by anybody. We have extended the algorithms for water bodies detection for the complete geographic region of agricultural land. Improved Faster R-CNN algorithm used in the detection of wells and farm ponds. More than 1000 Satellite imagery are used to train the model to detect the water body. The experimental results demonstrated promising accuracy on well and farm ponds detection. The accuracy of the faster RCNN algorithm is 90%, we have tested the accuracy of the results by GIS locations, and ground-truthing is achieved by our designed Android App.This app gives the latitude and longitude of well or farm ponds entered by the person in Jalgaon. Keywords : About four key words or phrases in alphabetical order, separated by commas.

Multifactorial Tomato Leaf Disease Detection Based on Improved YOLOV5

Target detection algorithms can greatly improve the efficiency of tomato leaf disease detection and play an important technical role in intelligent tomato cultivation. However, there are some challenges in the detection process, such as the diversity of complex backgrounds and the loss of leaf symmetry due to leaf shadowing, and existing disease detection methods have some disadvantages in terms of deteriorating generalization ability and insufficient accuracy. Aiming at the above issues, a target detection model for tomato leaf disease based on deep learning with a global attention mechanism, TDGA, is proposed in this paper. The main idea of TDGA includes three aspects. Firstly, TDGA adds a global attention mechanism (GAM) after up-sampling and down-sampling, as well as in the SPPF module, to improve the feature extraction ability of the target object, effectively reducing the interference of invalid targets. Secondly, TDGA uses a switchable atrous convolution (SAConv) in the C3 module to improve the model’s ability to detect. Thirdly, TDGA adopts the efficient IoU loss (EIoU) instead of complete IoU loss (CIoU) to solve the ambiguous definition of aspect ratio and sample imbalance. In addition, the influences of different environmental factors such as single leaf, multiple leaves, and shadows on the performance of tomato disease detection are extensively experimented with and analyzed in this paper, which also verified the robustness of TDGA. The experimental results show that the average accuracy of TDGA reaches 91.40%, which is 2.93% higher than that of the original YOLOv5 network, which is higher than YOLOv5, YOLOv7, YOLOHC, YOLOv8, SSD, Faster R-CNN, RetinaNet and other target detection networks, so that TDGA can be utilized for the detection of tomato leaf disease more efficiently and accurately, even in complex environments.

Process defect analysis and visual detection of aluminum/copper cable joints with magnetic pulse crimping

Magnetic pulse crimping (MPC) has been demonstrated to enhance the electrical and mechanical performance of the aluminum/copper cable joints compared to traditional mechanical crimping. However, little attention has been given to the impact of defects during the manufacture on the performance. This paper uses MPC technology to connect joints and investigates the influence of various defects (including scratches, skew, length inadequate, wire broken, and depth inadequate), on the electrical and mechanical performance of the joints. The results indicated that the defects lead to significant changes performance of joints. Compared to the normal joint, skew, length inadequate, wire broken, and depth inadequate contributed to decreased tightness and the strength of the joints, resulting in an increase in the average resistance by 518.8 %, 1133.2 %, 1533.8 %, and 1377.9 %. Simultaneously, average loads of joints decrease by 16.4 %, 23.5 %, 28.8 %, and 91.3 %, respectively. Scratches have no discernible impact on performance. To address these defects systematically, a hierarchical defect detection approach was proposed. Faster R-CNN was employed to search three distinct regions: a large region (L), a middle region (M), and a small region (S), each associated with different defects that proved to impact performance via previous experiments. A COCO metric of 84.85 % was achieved. Four ResNet models were then utilized to classify defects within these regions. ResNet50 exhibited the best overall performance: 95 % accuracy for L, 100 % for M, and 87 % for S.