Study on Combining Two Faster R-CNN Models for Landslide Detection with a Classification Decision Tree to Improve the Detection Performance

Detection of metastasis of mediastinal lymph nodes in lung cancer patients with an artificial intelligence model.

WITHDRAWN: MRI based detection and classification of brain tumor using enhanced faster R-CNN and Alex Net model

Accurate occupancy information is crucial for enhancing energy efficiency and reducing carbon emissions in buildings. However, the inherent unpredictability of occupants introduces uncertainties in energy analysis and control strategy development. To address these challenges, this study proposes a vision-based method employing state-of-the-art deep learning models to capture real-time occupancy profiles in crowded indoor spaces. Utilising a self-collected image dataset, various deep learning models, including Single Shot MultiBox Detector (SSD), Faster Region-based Convolutional Neural Networks (Faster R-CNN), and different versions of You Only Look Once (YOLO) were trained and evaluated. An experiment was conducted in a lecture room equipped with cameras and environmental sensors to evaluate the performance of each model in terms of precision, computational efficiency, and adaptability to varying occupancy levels during a lecture session. The session included varying occupancy conditions: entering (barely occupied), during the lecture (typical occupancy), and leaving the room (again barely occupied). Among the models tested, YOLOv8x exhibited the best performance in terms of accuracy, while SSD lagged notably. The impact on the detection performance of various locations of camera setups was also explored. Energy simulations revealed that deep learning-based model generated occupancy profiles significantly deviated from conventional “fixed” occupancy profiles, resulting in a 13.45 % variation in predicted heating energy demand. However, compared to the ground truth, these profiles showed minimal variation (up to 6.72 %) for the Faster R-CNN and YOLO models, highlighting their accuracy and robustness. Additionally, although the deep learning-based occupancy profiles generally overpredicted the recorded data, the CO2 concentration trends they predicted aligned closely with the recorded data, unlike the “fixed” occupancy profiles. The findings underscore the importance of realistic occupancy profiles for reliable energy predictions in buildings and demonstrate the potential of the proposed vision-based method for advancing occupancy detection and building energy management.

Grouper and snapper fish are widely consumed species with high economic value in the global market. To determine their economic value, identifying the species and estimating the weight are essential in the pricing and quality determination of the traded fish. The commonly used manual methods are often time-consuming and labor-intensive. Based on this, a more effective computer-based method is needed for these repetitive tasks. This research aims to analyze the performance of two commonly used deep learning models, YOLO and Faster R-CNN, in detecting species and estimating the weight of specific grouper and snapper fish. The data used consisted of 2991 samples divided into 18 classes. This data was then augmented using rotate and flip features to create 6843 image samples. A threshold of 0.8 was used in the detection process, meaning objects detected with confidence below 0.8 would be ignored. Once trained, the performance of both models was tested using precision, recall, and accuracy parameters to assess how accurately the models predicted fish species from the input data and Mean Absolute Percentage Error (MAPE) to evaluate the estimation results of the models. There were differences in the quantitative evaluation results between the YOLO and Faster R-CNN models. The YOLO model achieved precision, recall, and accuracy rates of 0.98, 0.98, and 0.96, respectively, while the Faster R-CNN model had precision, recall, and accuracy rates of 0.97, 0.98, and 0.95, respectively. Additionally, the MAPE for weight estimation was 2.42% for image data and 3.66% for video data for the YOLO model. In contrast, for the Faster R-CNN model, the results were 14.62% for image data and 13.59% for video data. Thus, it can be concluded that the YOLO model provides better quantitative evaluation results compared to the Faster R-CNN model.

Accurate tree counting in plantations is essential for effective management and resource allocation, yet traditional manual digitization methods are labor-intensive and time-consuming. This study presents an innovative approach to automate tree counting in oil palm plantations using UAV/drone orthophoto imagery and the Faster R- CNN algorithm implemented in Python. High-resolution images were captured using UAVs and pre-processed for clarity. The Faster R-CNN model was trained on annotated images with manually labeled bounding boxes. Leveraging a two-stage pipeline, the model generates region proposals and classifies objects within them. Performance evaluation using precision, recall, and Intersection over Union (IoU) metrics demonstrated the model's high accuracy, achieving an average precision (AP) of 75% and an average recall (AR) of 44%. The automated process significantly reduces labor costs and time compared to traditional methods. This study highlights the effectiveness of integrating UAV technology with machine learning for agricultural applications, providing a scalable solution adaptable to various plantations and tree species.

Development of image-based wheat spike counter through a Faster R-CNN algorithm and application for genetic studies

Wind turbine generator system plays a fundamental role in electricity generation in industry 4.0, and wind turbines are usually distributed separately and in poor locations. Unmanned Aerial Vehicles (UAV) which could overcome the above challenges are deployed to collect photographs of wind turbines, could be used for predictive maintenance of wind turbines and energy management. However, identifying meaningful information from huge amounts of photographs taken by drones is a challenging task due to various scales, different viewpoints, and tedious manual annotation. Besides, deep neural networks (DNN) are dominant in object detection, and training DNN requires large numbers of accurately labeled training data, and manual data annotation is tedious, inefficient, and error-prone. Considering these issues, we generate a synthetic UAV-taken dataset of wind turbines, which provides RGB images, target bounding boxes, and precise pixel annotations as well. But directly transferring the model trained on the synthetic dataset to the real dataset may lead to poor performance due to domain shifts (or domain gaps). The predominant approaches to alleviate the domain discrepancy are adversarial feature learning strategies, which focus on feature alignment for style (e.g., color, texture, illumination, etc.) gaps without considering the content (e.g., densities, backgrounds, and layout scenes) gaps. In this study, we scrutinize the real UAV-taken imagery of wind turbines and develop a synthetic generation method trying to simulate the real ones from the aspects of style and content. Besides, we propose a novel soft-masks guided faster region-based convolutional neural network (SMG Faster R-CNN) for domain adaptation in wind turbine detection, where the soft masks help to extract highly object-related features and suppress domain-specific features. We evaluate the accuracy of SMG Faster R-CNN on the wind turbine dataset and demonstrate the effectiveness of our approach compared with some prevalent object detection models and some adversarial DA models.

The aim of this study was to compare the performance of a deep-learning convolutional neural network (Faster R-CNN) model to detect imaging findings suggestive of idiopathic Parkinson’s disease (PD) based on [18F]FP-CIT PET maximum intensity projection (MIP) images versus that of nuclear medicine (NM) physicians. The anteroposterior MIP images of the [18F]FP-CIT PET scan of 527 patients were classified as having PD (139 images) or non-PD (388 images) patterns according to the final diagnosis. Non-PD patterns were classified as overall-normal (ONL, 365 images) and vascular parkinsonism with definite defects or prominently decreased dopamine transporter binding (dVP, 23 images) patterns. Faster R-CNN was trained on 120 PD, 320 ONL, and 16 dVP pattern images and tested on the 19 PD, 45 ONL, and seven dVP patterns images. The performance of the Faster R-CNN and three NM physicians was assessed using receiver operating characteristics curve analysis. The difference in performance was assessed using Cochran’s Q test, and the inter-rater reliability was calculated. Faster R-CNN showed high accuracy in differentiating PD from non-PD patterns and also from dVP patterns, with results comparable to those of NM physicians. There were no significant differences in the area under the curve and performance. The inter-rater reliability among Faster R-CNN and NM physicians showed substantial to almost perfect agreement. The deep-learning model accurately differentiated PD from non-PD patterns on MIP images of [18F]FP-CIT PET, and its performance was comparable to that of NM physicians.

In the past few years, automatic building detection in aerial images has become an emerging field in computer vision. Detecting the specific types of houses will provide information in urbanization, change detection, and urban monitoring that play increasingly important roles in modern city planning and natural hazard preparedness. In this paper, we demonstrate the effectiveness of detecting various types of houses in aerial imagery using Faster Region-based Convolutional Neural Network (Faster-RCNN). After formulating the dataset and extracting bounding-box information, pre-trained ResNet50 is used to get the feature maps. The fully convolutional Region Proposal Network (RPN) first predicts the bounds and objectness score of objects (in this case house) from the feature maps. Then, the Region of Interest (RoI) pooling layer extracts interested regions to detect objects that are present in the images. To the best of our knowledge, this is the first attempt at detecting houses using Faster R-CNN that has achieved satisfactory results. This experiment opens a new path to conduct and extent the works not only for civil and environmental domain but also other applied science disciplines.

The development of mathematical models for detecting moving objects in real-time video streams is a significant task, especially in light of the growing demands for security and process automation. This paper proposes a detection system based on the Faster Region-Based Convolutional Neural Network (Faster R-CNN) model, utilizing various backbone architectures, including Residual Network-50 (ResNet-50), Feature Pyramid Network (FPN), MobileNet Version 3 Large, and Efficient Network-B0 (EfficientNetB0) with a Self-Attention mechanism. The model extracts object features by combining deep neural networks and scaling mechanisms, enabling adaptation to various operating conditions. Experimental results demonstrate that the proposed approach achieves high accuracy, with the MobileNet Version 3 Large model achieving 95.70% accuracy and the Residual Network-50 model reaching 100% accuracy within the first three training cycles (epochs). The Faster Region-Based Convolutional Neural Network model using Efficient Network-B0 with the Self-Attention mechanism reaches 100% accuracy by the third training cycle, maintaining consistent performance throughout the remaining training cycles. The model with the Residual Network-50 and Feature Pyramid Network backbone architecture shows an average loss reduction from 0.0922 in the first training cycle to 0.0102 by the 15th training cycle, confirming its high stability and performance. Future research could focus on optimizing the proposed approach to handle even more complex and dynamic video sequences and improving data processing to reduce computational costs and increase processing speed.

The most important aspect of automatic driving and traffic surveillance is vehicle detection. In addition, poor road conditions caused by potholes are the cause of traffic accidents and vehicle damage. The proposed work uses deep learning models. The proposed method can detect vehicles and potholes using images. The faster region-based convolutional neural network (CNN) and the inception network V2 model are used to implement the model. The proposed work compares the performance, accuracy numbers, detection time, and advantages and disadvantages of the faster region-based convolution neural network (Faster R-CNN) with single shot detector (SSD) and you only look once (YOLO) algorithms. The proposed method shows good progress than the existing methods such as SSD and YOLO. The measure of performance evaluation is Accuracy. The proposed method shows an improvement of 5% once compared with the previous methods such as SSD and YOLO.

With the application and development of photovoltaic (PV) power generation, defects in its operation can significantly reduce the efficiency of power generation, so infrared thermography is crucial for hot spot defect detection in PV modules. In this paper, an improved Faster Region-based Convolutional Neural Network (Faster R-CNN) algorithm is proposed for automatic hot spot detection in aerial infrared images. First, the anchor frame size configuration is optimized based on the clustering algorithm to address the challenges of small hot spot target size and complex background. Subsequently, the ResNet-50 backbone network is used instead of the original network to enhance the hot spot feature extraction capability. Finally, it is trained and tested on the self-constructed thermal image dataset, and the experimental results show that the detection accuracy of this method is improved compared with the traditional method, which provides technical support for the intelligent operation and maintenance of PV on the UAV edge computing platform.

In the last couple of years, advancements in the deep learning, especially in convolutional neural networks, proved to be a boon for the image classification and recognition tasks. One of the important practical applications of object detection and image classification can be for security enhancement. If dangerous objects or scenes can be identified automatically, then a lot of accidents can be prevented. For this purpose, in this paper we made use of state-of-the-art implementation of Faster Region-based Convolutional Neural Network (Faster R-CNN) based on the monitoring video of hoisting sites to train a model to detect the dangerous object and the worker. By extracting the locations of them, object-human interactions during hoisting, mainly for changes in their spatial location relationship, can be understood whereby estimating whether the scene is safe or dangerous. Experimental results showed that the pre-trained model achieved good performance with a high mean average precision of 97.66% on object detection and the proposed method fulfilled the goal of dangerous scenes recognition perfectly.

This study aimed to develop and evaluate an artificial intelligence (AI) framework for detecting dental restorations and prosthesis devices on panoramic radiographs (PRs). Detecting these elements is essential for enhancing automated reporting, improving the accuracy of dental assessments, and reducing manual examination time. A fast region-based convolutional neural network (Fast R-CNN) was trained using 186 PRs for the training set and 42 for validation. The model's performance was assessed on an external test dataset of 1133 PRs. Seven dental restorations and prosthesis devices were targeted: appliance, bridge, endodontic filling, crown filling, implant, retainer, and single crown. Precision, recall, and F1-score were calculated for each element to measure detection accuracy. The AI framework achieved high performance across all categories, with precision, recall, and F1-scores as follows: appliance (0.79, 0.96, 0.87), bridge (0.91, 0.86, 0.89), endodontic filling (0.98, 0.98, 0.98), crown filling (0.95, 0.95, 0.95), implant (0.99, 0.97, 0.98), retainer (0.98, 0.98, 0.98), and single crown (0.94, 0.96, 0.95). The system processes one panoramic image in under 30 seconds. The AI framework demonstrated high recall and efficiency in detecting dental prosthesis and other dental restorations on PRs. Its application could significantly streamline dental diagnostics and automated reporting, enhancing both the speed and accuracy of dental assessments. This study highlights the potential of AI in automating the detection of multiple dental restorations and prosthesis on PRs, offering a valuable tool for dental professionals to improve diagnostic workflows.

Computer vision is developing really fast in recent years, and object detection is now one of the hottest topics. faster region-based convolutional neural network (faster R-CNN) and You Only Look Once (YOLOv3) are two popular modules to solve object detection problems, but they are implemented in different ways and thus have different performance in practice. This article is going to introduce these two modern modules and make a few experiments to compare the performance of each module with various datasets. This analysis will focus on speed, accuracy and the performances in different situations. Two specific studies will be mentioned as two typical examples of object detection application: face mask detection and greenhouse detection from satellite images. At last, the article will draw out a conclusion, make some suggestions for the choice of faster R-CNN and YOLOv3 and make a prospect for the future. It turns out that generally faster R-CNN fits tasks that require high accuracy better and YOLOv3 can realize real-time detection tasks.