Energy conservation for existing cooling and lighting loads

The YOLOv8 model has high detection efficiency and classification accuracy in detecting commutator surface defects, aimed at the problem of low working efficiency of a commutator, caused by commutator surface defects. First, the theoretical framework of Region-based Convolutional Neural Networks (R-CNN), spatial pyramid pooling (SPP)-net, Fast R-CNN, and Faster R-CNN is introduced, and the detection principle and process are described in detail. Secondly, the principle of the YOLOv8 network structure, head structure, neck structure, and C2f module are explained, and the loss function is described. The average precision of the proposed algorithm for detecting cracks and small points is more than 98%, and the frames per second (FPS) is 27. The detection results are mapped to the original image, and the visualization of the commutator surface defect detection is obtained, which has a higher robustness, accuracy, and real-time performance than the R-CNN, SPP-net, Fast R-CNN, and Faster R-CNN algorithms.

The automation of agricultural practices offers significant potential for boosting productivity while reducing labor requirements, particularly in fruit harvesting. However, accurately detecting tomatoes in dynamic and complex field environments remains a challenge due to issues such as high false positive rates, missed detections, variable illumination, occlusion, and heterogeneous foliage. In this research, tomato recognition for autonomous picking robots is conducted using the deep learning model Faster R-CNN (Region-based Convolutional Neural Network). Faster R-CNN excels in object detection tasks within complex scenes containing multiple objects and diverse backgrounds by generating region proposals and performing classifications, thereby addressing these challenges effectively. The Faster R-CNN model is trained and optimized on a comprehensive dataset of tomato plants collected under diverse field conditions for real-time detection. To enhance robustness and reliability in challenging environments, sensor fusion techniques integrating RGB and depth information are employed. This approach is expected to significantly reduce false positives and missed detections, improving the overall efficiency of autonomous picking robots. The proposed solution not only addresses critical inefficiencies in the tomato harvesting process but also advances the application of deep learning models in precision agriculture, promoting sustainable and efficient farming practices.

Coronavirus disease 2019 (COVID‐19) has a significant impact on human life. The novel pandemic forced humans to change their lifestyles. Scientists have broken through the vaccine in many countries, but the face mask is the only protection for public interaction. In this study, deep neural networks (DNN) have been employed to determine the persons wearing masks correctly. The faster region‐based convolutional neural networks (RCNN) model has been used to train the data using graphics processing unit (GPU) device. To achieve our goals, we used a multiphase detection model: first, to label the face mask, and second to detect the edge and compute edge projection for the chosen face region within the face mask. The current findings revealed that faster RCNN was efficient and precise, giving 97% accuracy. The overall loss after 200,000 epochs is 0.0503, with a trend to decrease. While the loss is falling, we are getting more accurate results. As a result, the faster RCNN technique effectively identifies whether a person is wearing face masks or not, and the training period was decreased with better accuracy. In the future, Deep Neural Network (DNN) might be used first to train the data and then compress the dimensions of the input to run it on low‐powered devices, resulting in a lower computational cost. Our proposed system can achieve high face detection accuracy and coarsely obtain face posture estimation based on the specified rule. The faster RCNN learning algorithm returns high precision, and the model’s lower computational cost is achieved on GPU. We use the “label‐image” application to label the photographs extracted from the dataset and apply Inception V2 of faster RCNN for face mask detection and classification.

Recent development in object detection are greatly driven by the success of region proposal approaches and region-based convolutional neural networks (R-CNNs). In this paper, we designed and implemented an object detection system using a faster-CNN method that shares full-image convolutional features with a detection network, so as to enable nearly cost-free region proposals. Development of this system is based on the previous work on Faster R-CNN. Results shows that with this method, we could achieve high accuracy while detecting objects.

Distinguishing between primary clear cell carcinoma of the liver (PCCCL) and common hepatocellular carcinoma (CHCC) through traditional inspection methods before the operation is difficult. This study aimed to establish a Faster region-based convolutional neural network (RCNN) model for the accurate differential diagnosis of PCCCL and CHCC. In this study, we collected the data of 62 patients with PCCCL and 1079 patients with CHCC in Beijing YouAn Hospital from June 2012 to May 2020. A total of 109 patients with CHCC and 42 patients with PCCCL were randomly divided into the training validation set and the test set in a ratio of 4:1.The Faster RCNN was used for deep learning of patients' data in the training validation set, and established a convolutional neural network model to distinguish PCCCL and CHCC. The accuracy, average precision, and the recall of the model for diagnosing PCCCL and CHCC were used to evaluate the detection performance of the Faster RCNN algorithm. A total of 4392 images of 121 patients (1032 images of 33 patients with PCCCL and 3360 images of 88 patients with CHCC) were uesd in test set for deep learning and establishing the model, and 1072 images of 30 patients (320 images of nine patients with PCCCL and 752 images of 21 patients with CHCC) were used to test the model. The accuracy of the model for accurately diagnosing PCCCL and CHCC was 0.962 (95% confidence interval [CI]: 0.931-0.992). The average precision of the model for diagnosing PCCCL was 0.908 (95% CI: 0.823-0.993) and that for diagnosing CHCC was 0.907 (95% CI: 0.823-0.993). The recall of the model for diagnosing PCCCL was 0.951 (95% CI: 0.916-0.985) and that for diagnosing CHCC was 0.960 (95% CI: 0.854-0.962). The time to make a diagnosis using the model took an average of 4 s for each patient. The Faster RCNN model can accurately distinguish PCCCL and CHCC. This model could be important for clinicians to make appropriate treatment plans for patients with PCCCL or CHCC.

The objective of this paper is to detect tomato leaf disease using Faster region-based convolutional neural network (R-CNN). The tomato leaf disease recognition system utilizes a dataset consisting of healthy tomato leaves and eight leaf diseases, including early blight, late blight, leaf mold, mosaic virus, septoria, spider mites, yellow leaf curl virus, and leaf miner. The dataset is obtained from various sources, such as Kaggle, Google Images, Bing Images, and Roboflow Universe. Pre-processing techniques, including collage, tile, static crop, and resize, are applied to prepare the dataset for training. Data augmentation methods, such as flipping, 90° rotation, exposure adjustment, and hue modification, are applied to enhance the model’s robustness and generalize its performance. Specifically, we implemented Faster R-CNN as part of Detectron2 using its base models and configurations. The results demonstrate that the X101-FPN base model for Faster R-CNN with the default configurations of Detectron2 is efficient and general enough to be applied to defect detection. This approach results in an average precision (AP) detection score of 87.01% for validation results.

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.

Nematodes represent very abundant and the largest species diversity in the world. Nematodes, which live in a soil environment, possess several functions in agricultural systems. There are two huge groups of soil nematodes, a non-parasitic nematode, which contributes positively to ecological processes, and a plant-parasitic nematode, which cause various disease and reduces yield losses in the agricultural system. Early detection and classification in the agricultural area infected with plant-parasitic nematode and interpreting the soil level condition in this area required a fast and reliable detection system. However, nematode identification is challenging and time-consuming due to their similar morphology. This study applied a pre-trained faster region-based convolutional neural network (RCNN) for plant-parasitic and non-parasitic nematodes detection. These deep learning-based object detection models gave satisfactory results as the accuracy reached 87.5%.

WITHDRAWN: Effective utilization of YOLOv3 model for aircraft detection in Remotely Sensed Images

In the neck part of a two-stage object detection network, feature fusion is generally carried out in either a top-down or bottom-up manner. However, two types of imbalance may exist: feature imbalance in the neck of the model and gradient imbalance in the region of interest extraction layer due to the scale changes of objects. The deeper the network is, the more abstract the learned features are, that is to say, more semantic information can be extracted. However, the extracted image background, spatial location, and other resolution information are less. In contrast, the shallow part can learn little semantic information, but a lot of spatial location information. We propose the Both Ends to Centre to Multiple Layers (BEtM) feature fusion method to solve the feature imbalance problem in the neck and a Multi-level Region of Interest Feature Extraction (MRoIE) layer to solve the gradient imbalance problem. In combination with the Region-based Convolutional Neural Network (R-CNN) framework, our Balanced Feature Fusion (BFF) method offers significantly improved network performance compared with the Faster R-CNN architecture. On the MS COCO 2017 dataset, it achieves an average precision (AP) that is 1.9 points and 3.2 points higher than those of the Feature Pyramid Network (FPN) Faster R-CNN framework and the Generic Region of Interest Extractor (GRoIE) framework, respectively.

In order to ensure the safety of transmission lines, the use of unmanned aerial vehicle (UAV) images for automatic object detection has important application prospects, such as the detection of birds’ nests. The traditional bird’s nest detection methods mainly include the study of morphological characteristics of the bird’s nest. These methods have poor applicability and low accuracy. In this work, we propose a deep learning-based birds’ nests automatic detection framework—region of interest (ROI) mining faster region-based convolutional neural networks (RCNN). First, the prior dimensions of anchors are obtained by using k-means clustering to improve the accuracy of coordinate boxes generation. Second, in order to balance the number of foreground and background samples in the training process, the focal loss function is introduced in the region proposal network (RPN) classification stage. Finally, the ROI mining module is added to solve the class imbalance problem in the classification stage, combined with the characteristics of difficult-to-classify bird’s nest samples in the UAV images. After parameter optimization and experimental verification, the deep learning-based bird’s nest automatic detection framework proposed in this work achieves high detection accuracy. In addition, the mean average precision (mAP) and formula 1 (F1) score of the proposed method are higher than the original faster RCNN and cascade RCNN. Our comparative analysis verifies the effectiveness of the proposed method.

Most of the loads connected to the existing AC distribution system operate with low voltage DC power. Hence, there is a requirement to convert AC into DC power. Often there are many inefficiencies associated with AC to DC power conversion. To enhance the energy conservation, low voltage DC distribution system (LVDCDS) is suggested in this paper. Forty percent of the total power consumed in India is by lighting loads which make use of DC power. LVDCDS is an ideal choice to power lighting loads in residential and commercial buildings. This paper presents a detailed study of four different single stage, interleaved, non-isolated light emitting diode (LED) drivers for lighting load. All the topologies are developed in hardware and tested experimentally with controller implementation to provide a constant power of 70 W to power LED lights in the campus building. The installation cost, payback period, and energy savings for the converters connected to LVDCDS are analyzed and discussed in particular.

Mechanical parts often have surface defects such as scratches, cracks and pits during the production process, which seriously affect product quality and service life. To address this challenge, this paper proposes a method for detecting and quantifying surface defects of mechanical parts based on image processing. First, this paper uses high-resolution image acquisition technology to obtain surface images of mechanical parts, and improves image quality through preprocessing steps. Then, this paper uses edge detection, morphological operations, and image segmentation algorithms to extract the features of surface defects, thereby realizing automatic identification of defective areas. Finally, this paper combines the Faster R-CNN (Faster Region-Based Convolutional Neural Network) algorithm to further refine the classification and quantification of defects and obtain the specific location, size and type of defects. Experimental results show that among the 500 mechanical parts samples tested, the proposed method has a mean average precision (mAP) of 0.93 in surface defect detection. Compared with other image processing methods, this method has significantly improved defect detection accuracy and speed. Especially when processing higher-resolution images, the Faster R-CNN model can achieve efficient defect classification and quantification to meet the needs of real-time detection in industrial production.

Knowledge of the number and distribution of oil palm trees during the crop cycle is vital for sustainable management and predicting yields. The accuracy of the conventional image processing method is limited for the hand-crafted feature extraction method and the overfitting problem occurs due to the insufficient dataset. We propose a modification of the Faster Region-based Convolutional Neural Network (FRCNN) for palm tree detection to reduce the overfitting problem and improve the detection accuracy. The enhanced FRCNN (EFRCNN) leads to improved performance for detecting objects (in the same image) when they are of multiple sizes by using a feature concatenation method. Transfer learning based on a ResNet50 model is used to extract the features of the input image. High-resolution images of oil palm trees from a drone are used to form the data set, containing mature, young, and mixed oil palm tree regions. We train and test the EFRCNN, the FRCNN, a CNN used recently for oil palm image detection, and two standard methods, namely, the support vector machine (SVM) and template matching (TM). The results reveal an overall accuracy of ≥96.8% for the EFRCNN on the three test sets. The accuracy is higher than the CNN and FRCNN and substantially higher than SVM and TM. For large-scale plantations, the accuracy improvement is significant. This research provides a method for automatically counting the oil palm trees in large-scale plantations.

Heritage is the identity of any society, and it should be conserved for the coming generations. Heritage monuments and buildings are seriously threatened by environmental agencies such as moisture, intense solar radiation, prevailing winds, and rain, changing their physical attributes. These environmental agencies cause various defects like spalling, abrasion, cracks, stains, and fungal growth in the heritage monuments. Detection and documentation of these defects are processes of the condition survey, which should be required just before the commencement of repair and maintenance of any structure. Traditionally, these defects are detected and documented by experts on the field using paper-based surveys. However, these methods are time-consuming and require immense expertise and cost. This paper presents the novel approach of condition survey by automated defect detection system to overcome these limitations. The automated defect detection system is developed using the Faster R-CNN (Region-based Convolutional Neural Network) to detect multiple types of defects from the heritage monument photos. Moreover, the web-based interface is developed to run the automated condition survey system. Types and number of defects and the position of defects are the output of the developed condition survey system. The system is demonstrated for the national heritage monument located in Surat city to check the developed system's effectiveness and field application. The proposed system is automatic, fast, and reliable for a condition survey of heritage monuments. This system will be useful for conservation, preservation, and maintenance management of heritage monuments.KeywordsCondition surveyAutomatic defect detectionHeritage monumentsDeep learningFaster R-CNN