Faster R-CNN Algorithm Research Articles

Research on Surface Defect Detection Method of Wire Rope Outer Covering Layer Based on Deep Learning

Abstract Aiming at the requirement of a certain enterprise for the visual inspection accuracy of the surface defects of the outer sheath of the wire rope to reach 95 %, this paper adopts the existing Faster R-CNN algorithm to detect the surface defects of the outer sheath of the wire rope, and its accuracy mAP 0.5 is 8 8.7%. In order to improve the detection accuracy, the Faster R-CNN algorithm is improved. Among them, ResNeXt-101 is obtained on the basis of ResNeXt-50. It is used as the backbone network, and the attention mechanism feature pyramid network ACFPN is introduced to extract multi-scale features to enhance the network’s defect detection ability. The defect detection accuracy mAP 0.5 is improved to 93.2% In view of the diversity and large size differences of the defects of the outer sheath of the wire rope, a new loss function is constructed for the network. At the same time, the BO algorithm is used to optimize the hyperparameters of the stochastic gradient optimizer SGD of the improved network. The final designed ResNeXt101-ACFPN-Faster R-CNN algorithm is used to detect the surface defects of the outer sheath of the wire rope, and the detection accuracy mAP 0.5 reaches 95.8%. The detection goal of the enterprise is achieved. The research results show that the network optimization method proposed in this paper has a good effect on improving the detection accuracy of the surface defects of the outer sheath of the wire rope, and also verifies the effectiveness and feasibility of the algorithm improvement.

Wavelet neural network algorithm for hybrid GA in infrared CO2 gas sensor

As the economy develops and the environmental impact of the greenhouse effect becomes more apparent, the need for precise measurement of specific gas concentrations in the air has become increasingly pressing. Nevertheless, as a representative of greenhouse gases, CO2 gas detectors are susceptible to environmental temperature fluctuations, which impairs the accuracy of detection. To address this issue, the research team innovatively combined the genetic algorithm (GA) and the wavelet neural network (WNN) to develop a solution for the temperature compensation problem of the infrared CO2 gas sensor. The non-dominant sorted genetic algorithm II (NSGA-II) was integrated into the GA to achieve a balance between the accuracy, complexity, and temperature performance of the model through multi-objective optimization. The results showed that compared with other existing models, the GA-WNN model proposed in this study can significantly reduce the difference between the detected values and the actual environmental values under various temperature conditions when processing data. Especially at an ambient temperature of 49 °C, for a true CO2 concentration of 2000 ppm, the detection value processed by the GA-WNN algorithm was 2046 ppm, with a relative error of only 2.3 %, far lower than the 9.8 % of Faster RCNN algorithm and 11.5 % of WNN algorithm. The contribution of the research is the proposal of a novel temperature compensation method that significantly enhances the precision of infrared CO2 gas sensors. This is of paramount importance for enhancing the accuracy of gas detection in environmental monitoring and industrial control.

Improved Faster-RCNN Algorithm for Traffic Sign Detection

Improved Faster-RCNN Algorithm for Traffic Sign Detection

Prediction method of loess landslides based on faster R-CNN and WACM

Due to the complexity of the environment and geological conditions in which the loess slope is located, there are many challenges in the accuracy and prediction of loess landslide detection. Therefore, this study introduces a fast convolutional neural network model to solve the problems of traditional detection methods in terms of technology, cost, and detection accuracy, and to achieve real-time detection of the morphology of loess landslides. A weight absorption coupling model is constructed to address the uniform moisture content in loess with hidden dangers. Combined with instability probability, the probability of shallow loess landslides is predicted. The results showed that the mAP value of the Faster R-CNN algorithm using the ResNet125 network exceeded 90%, which was 46.23% and 32.01% higher than the algorithm models using ResNet50 and VGG16, respectively. The proposed model performed fractal analysis on four different loess particle samples, with correlation coefficients R2 above 0.9. The difference between the predicted and actual moisture content of upper and surface loess was within 11%. Compared with existing methods, the research and construction of a loess landslide detection and probability prediction model has greatly improved reliability and accuracy, which is of great significance for predicting the probability of different loess landslides.

An Optimized Object Detection Algorithm for Marine Remote Sensing Images

In order to address the challenge of the small-scale, small-target, and complex scenes often encountered in offshore remote sensing image datasets, this paper employs an interpolation method to achieve super-resolution-assisted target detection. This approach aligns with the logic of popular GANs and generative diffusion networks in terms of super-resolution but is more lightweight. Additionally, the image count is expanded fivefold by supplementing the dataset with DOTA and data augmentation techniques. Framework-wise, based on the Faster R-CNN model, the combination of a residual backbone network and pyramid balancing structure enables our model to adapt to the characteristics of small-target scenarios. Moreover, the attention mechanism, random anchor re-selection strategy, and the strategy of replacing quantization operations with bilinear interpolation further enhance the model’s detection capability at a low cost. Ablation experiments and comparative experiments show that, with a simple backbone, the algorithm in this paper achieves a mAP of 71.2% on the dataset, an improvement in accuracy of about 10% compared to the Faster R-CNN algorithm.

Rupiah Banknotes Detection Comparison of The Faster R-CNN Algorithm and YOLOv5

Money is an essential part of human life. Humans are never separated from activities related to money. As time goes by, money is not only a means of transactions between humans but also between humans and machines. Machines can recognize money in various ways, including object detection. Object detection is one of the most popular branches of computer vision. There are many methods for carrying out object detection, such as Faster R-CNN and YOLO. Faster R-CNN has been widely used in various fields to perform object detection tasks. Faster R-CNN has advantages over its predecessor because it uses a Region Proposal Network (RPN) as a substitute for selective search, which requires less compilation time. YOLO (You Only Look Once) is the most frequently used object detection method. This method divides the image into grids; each part of the grid predicts objects and their probabilities. The main advantages of YOLO are its high speed and ability to recognize objects in various conditions and positions with reasonably high accuracy. This research compares the Faster R-CNN algorithm model using the ResNet-50 architecture with YOLOv5 to recognize rupiah banknotes. The dataset used is 1120 images consisting of 8 classes. The YOLOv5 model trained on RGB data had the best results, with calculation accuracy reaching 1. Test results on three images also showed suitable results. The hope is that this research can be applied in other research to build a system for recognizing rupiah banknotes.

Road Design and Traffic Detection Methods for Autonomous Driving Scenarios

With the rapid promotion of autonomous driving technology, it is extremely important to scientifically anticipate the related technologies and analyze their possible impact on urban road systems. The accuracy of detection and localization of traffic elements of autonomous driving is closely related to the ability of autonomous driving devices to make control decisions and the safety of autonomous driving. The study designs a new high-speed road driving scheme based on autonomous driving by analyzing the challenges related to urban traffic that may be brought about by unmanned driving. On the basis of the faster R-CNN algorithm, the context information around the target is introduced to locate and detect small-scale traffic signs. A new pedestrian detection model is designed, which is based on the feature pyramid network and introduces the SE module to highlight the features of the visible part of the pedestrian and reduce the missed detection rate caused by inter-class occlusion. The improved traffic sign detection framework improves the detection accuracy by 18.91% compared to the original faster R-CNN, while the enhanced pedestrian inspection method improves the detection accuracy by 14.00%. For both traffic sign detection and pedestrian detection accuracy and speed are improved compared to the original method.

A Multi-Scale Target Detection Method Using an Improved Faster Region Convolutional Neural Network Based on Enhanced Backbone and Optimized Mechanisms.

Currently, existing deep learning methods exhibit many limitations in multi-target detection, such as low accuracy and high rates of false detection and missed detections. This paper proposes an improved Faster R-CNN algorithm, aiming to enhance the algorithm's capability in detecting multi-scale targets. This algorithm has three improvements based on Faster R-CNN. Firstly, the new algorithm uses the ResNet101 network for feature extraction of the detection image, which achieves stronger feature extraction capabilities. Secondly, the new algorithm integrates Online Hard Example Mining (OHEM), Soft non-maximum suppression (Soft-NMS), and Distance Intersection Over Union (DIOU) modules, which improves the positive and negative sample imbalance and the problem of small targets being easily missed during model training. Finally, the Region Proposal Network (RPN) is simplified to achieve a faster detection speed and a lower miss rate. The multi-scale training (MST) strategy is also used to train the improved Faster R-CNN to achieve a balance between detection accuracy and efficiency. Compared to the other detection models, the improved Faster R-CNN demonstrates significant advantages in terms of mAP@0.5, F1-score, and Log average miss rate (LAMR). The model proposed in this paper provides valuable insights and inspiration for many fields, such as smart agriculture, medical diagnosis, and face recognition.

The Impact of Tree Density on Automated Oil Palm Tree Counting Accuracy

The rapid development of oil palm plantations in Kualuh Leidong Subdistrict, Labuhanbatu Utara District, North Sumatra Province has led to an increased need for effective and efficient monitoring and supervision of oil palm trees. One method that supports such monitoring and supervision is the use of an automatic counting method using orthophoto data. This orthophoto data was used for automatic tree counting using a deep learning method with the Faster R-CNN algorithm. The study considered two planting patterns: regular planting patterns with spacing of 4 to 9 meters, and random planting patterns with varying spacing. Data processing involved an epoch value of 80 and a batch size value of 4. The accuracy of the automatic oil palm tree counting was evaluated based on the density of the spacing between trees with reference to the ground truth. The findings indicated that the deep learning Faster R-CNN algorithm achieved higher accuracy in automatic calculations for regular planting patterns.

A DL method to detect multi-type hidden objects in tunnel linings using a comprehensive GPR dataset

This study proposes an enhanced detection method for tunnel lining hidden objects based on the comprehensive Ground Penetrating Radar (GPR) dataset and an improved deep learning (DL) algorithm. To mitigate the issue of data scarcity, a tailored dataset consisting of six types of hidden objects was compiled, which contained 7426 on-site collected GPR images and 71,014 well-labeled objects. This bespoke dataset offered substantial support for algorithm training and validation, making it one of the most extensive datasets in the domain for tunnel lining GPR detection. An enhanced Faster R-CNN algorithm was then developed to effectively detect different types of hidden objects. Modifications were made in four key areas: Feature extraction module, Feature Pyramid Network (FPN), customized anchor scheme and modified loss function. This algorithm was subsequently trained and validated using the comprehensive dataset. Evaluation metrics demonstrated the performance of this approach, with a mean average Precision (mAP) of 81.2%. Results also proved particularly effective in detecting rebars, pipelines, and steel arches while demonstrating a shortfall in detecting voids and cavities due to the data limitation. The results underscore the potential of this enhanced algorithm for the automatic detection of diverse hidden objects in tunnel linings, thus stimulating further research in this vital field.

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.

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.

Autonomous navigation and collision prediction of port channel based on computer vision and lidar.

This study aims to enhance the safety and efficiency of port navigation by reducing ship collision accidents, minimizing environmental risks, and optimizing waterways to increase port throughput. Initially, a three-dimensional map of the port's waterway, including data on water depth, rocks, and obstacles, is generated through laser radar scanning. Visual perception technology is adopted to process and identify the data for environmental awareness. Single Shot MultiBox Detector (SSD) is utilized to position ships and obstacles, while point cloud data create a comprehensive three-dimensional map. In order to improve the optimal navigation approach of the Rapidly-Exploring Random Tree (RRT), an artificial potential field method is employed. Additionally, the collision prediction model utilizes K-Means clustering to enhance the Faster R-CNN algorithm for predicting the paths of other ships and obstacles. The results indicate that the RRT enhanced by the artificial potential field method reduces the average path length (from 500 to 430m), average time consumption (from 30 to 22s), and maximum collision risk (from 15 to 8%). Moreover, the accuracy, recall rate, and F1 score of the K-Means + Faster R-CNN collision prediction model reach 92%, 88%, and 90%, respectively, outperforming other models. Overall, these findings underscore the substantial advantages of the proposed enhanced algorithm in autonomous navigation and collision prediction in port waterways.

A falls recognition framework based on faster R-CNN and temporal action sequences that can be deployed on home service robots

With the popularity of home service robots (e.g. floor sweepers), robots should be considered to have more features for older adult care. Compared to fixed home monitors with a limited field of view, fall detection with service robots is an ideal solution to keep older adults and disabled people within sight. However, the user’s actions, such as lying on the bed to sleep or slumping on the sofa to rest, cause the traditional fall detection system to generate false alarms, which disrupts the user’s family life. The present work proposed an enhanced faster R-convolutional neural network (CNN) network by incorporating temporal action sequences and fall acceleration computation, demonstrating a reduced misjudgment rate on the service robot platform. Firstly, motion images were captured to obtain the target’s motion area description and action timing at the input stage. Then, the faster R-CNN algorithm was implemented to check the suspected falls further based on the falling acceleration of the detected actions during the training phase. Finally, the proposed temporal action sequences module eliminated the action mistaken for falling. Network training and robotic platform testing demonstrated that the proposed approach distinguished between falls and false alarms, which mitigated the occurrence of false positives. On the service robot platform, experimental results showed that the FAR was 8.19 and processing time was 0.79 s.

Intelligent Monitoring of Protection Devices in Power system with Enhanced Faster R-CNN

Relay protection devices are necessary to guard the power system safety and stability. With the significant number of substations and relay protection devices, the maintenance workload has become difficult due to limited manpower, posing hidden dangers to the reliable operation of protection devices. In view of this, this article proposes an enhanced Faster R-CNN algorithm to diagnose the relay protection devices based on image monitoring. The proposed algorithm uses RestNet50 as the main tool to recognize features from input images to generate feature maps. Meanwhile, to improve the image detection accuracy, the Non-Maximum Sup-pression (NMS) algorithm in regional suggestion network (RPN) is optimized from solid thresh-old to soft threshold. By combining the images captured by the surveillance camera, intelligent inspections are conducted on the status of the pressing plate, fiber bending, external object retention, and other items in the relay protection device. A great number of samples are used to train networks for different tasks. The results indicate that the enhanced Faster R-CNN has a higher image recognition rate, and the recognition accuracy of each inspection item for the protection device is higher than 95%.

Accurate multiclassification and segmentation of gastric cancer based on a hybrid cascaded deep learning model with a vision transformer from endoscopic images

Compared with other forms of cancer, gastric cancer has high mortality and incidence rates, making it a major cause of death worldwide. Accurate diagnosis is crucial in the treatment of stomach cancer. Researchers have used deep learning techniques facilitated by developments in artificial intelligence to classify and segment endoscopic images of stomach cancer. Most recentresearch examining endoscopic images of stomach cancer has used a binary classification system, whichis insufficient for practical use. False-positives and computational costs become problematic when segmentation is applied to all the images of healthy patients obtained throughout the evaluation. Hence, the expected level of performance has not been attained in the real-time multiclassification and segmentation of stomach cancer. In this study, we present a deep learning-based technique for multiclassification of endoscopic images by combiningmodified GoogLeNet and vision transformer (ViT) models and identifying invasive areas based on Faster R-CNN. The classification of endoscopic images into three categories, namely, normal, early gastric cancer, and advanced gastric cancer, is accomplished by using a hybrid approach including modified GoogLeNet and vision transformer (ViT) models. Gastric cancer regionsare then identified and segmented in the endoscopic images using the Faster R-CNN method. The Faster R-CNN algorithm is used with an endoscopic image as input, resulting in the generation of a bounding box and label image that accurately represents the gastric cancerous area. The proposed model achieved an accuracy, sensitivity and F1-score of 97.4%, 97.5% and 95.9%, respectively, for the classification of noncancerous, early gastric cancer and advanced gastric cancer. Furthermore, the performance of the segmentation method was also validated based on evaluation metrics and achieved 96.7%, 96.6% and 95.5% accuracy, sensitivity and F1-score, respectively, for the segmentation of noncancerous, early gastric cancer and advanced gastric cancer tissues. In conclusion, the method proposed in this study demonstrates enhanced global classification and detection performance compared to existing state-of-the-art algorithms. This finding underscores the significant potential of the proposed methodin the domain of gastric endoscopic image classification and segmentation.

Research on Surface Defect Detection Method of Photovoltaic Power Generation Panels——Comparative Analysis of Detecting Model Accuracy

INTRODUCTION: Research on intelligent defect detection technology using machine vision was conducted to address the challenging problem of detecting and localizing PV defects in photovoltaic power generation system operation and maintenance. OBJECTIVES: The aim is to improve the accuracy of PV defect detection and enhance the operation and maintenance efficiency of PV power plants. METHODS: In this paper, three detection methods such as image processing based detection, traditional machine learning based detection, and deep learning algorithm based detection are discussed and compared, and analyzed respectively. It is finally concluded that the deep learning based detection is more efficient in comparison. Then further analysis and simulation experiments are done through several detection algorithms based on deep learning. RESULTS: The experiment yields a high accuracy of the detection model based on the Faster-RCNN algorithm. Its mAP value reaches 92.6%. The detection model based on the YOLOv5 algorithm reaches a mAP value of 91.4%. But its speed is as much as 7 times faster than the model based on the Faster-RCNN algorithm. CONCLUSION: Comprehensive speed and accuracy index. Combining the needs of PV defect detection in the operation and maintenance of PV power generation systems with the results of simulation experiments. It is concluded that the detection model based on the YOLOv5 algorithm can provide better detection capability. Modeling with this algorithm is more suitable for PV defect detection.

Advanced Waste Seclusion and Chucking System Using Deep Learning Techniques

Any material that is undesirable or unusable is considered waste. Waste can be in any form (Liquid, solid or gas) but generally, waste is a solid form. There are various types of waste like paper, displeasing food, torn clothes, dried plants, kitchen waste, etc., Skin disease, diarrhea, tuberculosis, whooping cough, pneumonia etc.., are some other common diseases spread due to immoral waste management. Separating waste allows us to salvage more items, preventing their scraping in landfills. By reducing landfills disposal Segregating waste is important not only to reduce its impact on the environment but also to prevent health issues that can arise from the disposal of waste and toxins. Although the problem of waste segregation is a big challenge, there are several methods for automatic segregation, which eliminates the need for human hands. The proposed system utilizes Faster R-CNN algorithms to segregate waste into bio-degradable and non-biodegradable categories which, effectively eliminating the need for human involvement in the segregation process. This method employs artificial intelligence techniques to achieve waste segregation.

Detection of Safety Signs Using Computer Vision Based on Deep Learning

Safety signs serve as an important information carrier for safety standards and rule constraints. Detecting safety signs in mines is essential for automatically early warning of unsafe behaviors and the wearing of protective equipment while using computer vision techniques to realize advanced safety in the AI and IoT era. This work aims to propose an improved YOLOV4-tiny safety signs detection model applying deep learning to detect safety signs in mines. The dataset employed in this study was derived from coal mines and analogous environments, comprising a total of ten types of safety signs. It was partitioned into training, validation, and test sets following a distribution ratio of (training set + validation set) to test set = 9:1, with the training set to validation set ratio also set at 9:1. Then the attention mechanism ECANet was introduced into the model, which strengthened the network’s learning of places that need attention. Moreover, the Soft-NMS algorithm was used to retain more correct prediction frames and optimize the detection model to further improve the detection accuracy. The Focal Loss function was introduced to alleviate the problem of category imbalance in one-stage safety signs detection. Experimental results indicate that the proposed model achieved a detection precision of 97.76%, which is 7.55% and 9.23% higher than the YOLOV4-tiny and Faster RCNN algorithms, respectively. Besides, the model performed better in the generalization because it avoided the over-fitting phenomenon that occurred in the YOLOV4-tiny and the Faster RCNN. Moreover, the advantages of the improved model were more prominent when detecting small target areas and targets under dim conditions in coal mines. This work is beneficial for the intelligent early warning system with surveillance cameras in coal mines.

Automatic detection method for tobacco beetles combining multi-scale global residual feature pyramid network and dual-path deformable attention

Aiming at the problems of identifying storage pest tobacco pest beetles from images that have few object pixels and considerable image noise, and therefore suffer from lack of information and identifiable features, this paper proposes an automatic monitoring method of tobacco beetle based on Multi-scale Global residual Feature Pyramid Network and Dual-path Deformable Attention (MGrFPN-DDrGAM). Firstly, a Multi-scale Global residual Feature Pyramid Network (MGrFPN) is constructed to obtain rich high-level semantic features and more complete information on low-level features to reduce missed detection; Then, a Dual-path Deformable receptive field Guided Attention Module (DDrGAM) is designed to establish long-range channel dependence, guide the effective fusion of features and improve the localization accuracy of tobacco beetles by fitting the spatial geometric deformation features of and capturing the spatial information of feature maps with different scales to enrich the feature information in the channel and spatial. Finally, to simulate a real scene, a multi-scene tobacco beetle dataset is created. The dataset includes 28,080 images and manually labeled tobacco beetle objects. The experimental results show that under the framework of the Faster R-CNN algorithm, the detection precision and recall rate of this method can reach 91.4% and 98.4% when the intersection ratio (IoU) is 0.5. Compared with Faster R-CNN and FPN, when the intersection ratio (IoU) is 0.7, the detection precision is improved by 32.9% and 6.9%, respectively. The proposed method is superior to the current mainstream methods.