Improved Mask R-CNN with distance guided intersection over union for GPR signature detection and segmentation

Hou, Feifei; Lei, Wentai; Li, Shuai; Xi, Jingchun; Xu, Min; Luo, Jiabin

doi:10.1016/j.autcon.2020.103414

Cited by 75 publications

(22 citation statements)

References 34 publications

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“…e IOU threshold is given, and all detection frames whose IOU with the real frame are greater than 0.5 are adopted as samples [25]. e calculation of IOU is as follows:…”

Section: Performance Verificationmentioning

confidence: 99%

[Retracted] Adoption of Convolutional Neural Network Algorithm Combined with Augmented Reality in Building Data Visualization and Intelligent Detection

Wei

Tang

et al. 2021

Complexity

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It aims to improve the degree of visualization of building data, ensure the ability of intelligent detection, and effectively solve the problems encountered in building data processing. Convolutional neural network and augmented reality technology are adopted, and a building visualization model based on convolutional neural network and augmented reality is proposed. The performance of the proposed algorithm is further confirmed by performance verification on public datasets. It is found that the building target detection model based on convolutional neural network and augmented reality has obvious advantages in algorithm complexity and recognition accuracy. It is 25 percent more accurate than the latest model. The model can make full use of mobile computing resources, avoid network delay and dependence, and guarantee the real-time requirement of data processing. Moreover, the model can also well realize the augmented reality navigation and interaction effect of buildings in outdoor scenes. To sum up, this study provides a research idea for the identification, data processing, and intelligent detection of urban buildings.

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“…e IOU threshold is given, and all detection frames whose IOU with the real frame are greater than 0.5 are adopted as samples [25]. e calculation of IOU is as follows:…”

Section: Performance Verificationmentioning

confidence: 99%

[Retracted] Adoption of Convolutional Neural Network Algorithm Combined with Augmented Reality in Building Data Visualization and Intelligent Detection

Wei

Tang

et al. 2021

Complexity

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“…The Predicted Boundary Box is the bounding box that the Deep Learning model places around the object predicted. In fact, the model's projected bounding box is extremely unlikely to be an exact primary bounding box [60]. As a result, we can use the metric Intersection Over Union (IOU) to determine how accurately the object has been identified in the Image/Frame.…”

Section: -1-non-max Suppressionmentioning

confidence: 99%

Real-Time Monitoring of COVID-19 SOP in Public Gathering Using Deep Learning Technique

et al. 2021

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Crowd management has attracted serious attention under the prevailing pandemic conditions of COVID-19, emphasizing that sick persons do not become a source of virus transmission. World Health Organization (WHO) guidelines include maintaining a safe distance and wearing a mask in gatherings as part of standard operating procedures (SOP), considered thus far the most effective preventive measures to protect against COVID-19. Several methods and strategies have been used to construct various face detection and social distance detection models. In this paper, a deep learning model is presented to detect people without masks and those not keeping a safe distance to contain the virus. It also counts individuals who violate the SOP. The proposed model employs the Single Shot Multi-box Detector as a feature extractor, followed by Spatial Pyramid Pooling (SPP) to integrate the extracted features to improve the model's detecting capabilities. The MobilenetV2 architecture as a framework for the classifier makes the model highly light, fast, and computationally efficient, allowing it to be employed in embedded devices to do real-time mask and social distance detection, which is the sole objective of this research. This paper's technique yields an accuracy score of 99% and reduces the loss to 0.04%. Doi: 10.28991/esj-2021-SPER-14 Full Text: PDF

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“…The last option would be to utilize recent advancements in deep-learning object detection algorithms like convolutional neural networks (CNNs). CNNs have been proven as valuable object detectors for a variety of applications, including detecting sea scallops from benthic imagery [32]; detecting object signatures from ground penetrating radar [33]; detecting archaeological sites from LiDAR DEMs [34]; detecting ice-wedge polygons from aerial imagery [35]; detecting rocks from aerial imagery [36]; detecting mining-related valley fill faces from LiDAR DEMs [37]; and detecting airplanes, tennis courts, basketball courts, baseball diamonds and vehicles from aerial imagery [38]. CNNs are also fast, with models like Yolo and Faster R-CNN that can For the selection of the right detection method for Carolina Bays, one might first try some traditional image processing techniques, including Hough transforms, blob detectors like the Laplacian of Gaussian or difference of Gaussians, or feature detectors like the scale-invariant feature transform (SIFT).…”

Section: Traditional Computer Vision Pixel-based Classification and Object Detectionmentioning

confidence: 99%

“…CNNs have been proven as valuable object detectors for a variety of applications, including detecting sea scallops from benthic imagery [32]; detecting object signatures from ground penetrating radar [33]; detecting archaeological sites from LiDAR DEMs [34]; detecting ice-wedge polygons from aerial imagery [35]; detecting rocks from aerial imagery [36]; detecting mining-related valley fill faces from LiDAR DEMs [37]; and detecting airplanes, tennis courts, basketball courts, baseball diamonds and vehicles from aerial imagery [38]. CNNs are also fast, with models like Yolo and Faster R-CNN that can run through and detect objects in several images per second, up to 65 frames per second for Yolov4 on a Tesla V100 graphical processing unit (GPU) [39].…”

Section: Traditional Computer Vision Pixel-based Classification and Object Detectionmentioning

confidence: 99%

Using Convolutional Neural Networks for Detection and Morphometric Analysis of Carolina Bays from Publicly Available Digital Elevation Models

Lundine

Trembanis

2021

Remote Sensing

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Carolina Bays are oriented and sandy-rimmed depressions that are ubiquitous throughout the Atlantic Coastal Plain (ACP). Their origin has been a highly debated topic since the 1800s and remains unsolved. Past population estimates of Carolina Bays have varied vastly, ranging between as few as 10,000 to as many as 500,000. With such a large uncertainty around the actual population size, mapping these enigmatic features is a problem that requires an automated detection scheme. Using publicly available LiDAR-derived digital elevation models (DEMs) of the ACP as training images, various types of convolutional neural networks (CNNs) were trained to detect Carolina bays. The detection results were assessed for accuracy and scalability, as well as analyzed for various morphologic, land-use and land cover, and hydrologic characteristics. Overall, the detector found over 23,000 Carolina Bays from southern New Jersey to northern Florida, with highest densities along interfluves. Carolina Bays in Delmarva were found to be smaller and shallower than Bays in the southeastern ACP. At least a third of Carolina Bays have been converted to agricultural lands and almost half of all Carolina Bays are forested. Few Carolina Bays are classified as open water basins, yet almost all of the detected Bays were within 2 km of a water body. In addition, field investigations based upon detection results were performed to describe the sedimentology of Carolina Bays. Sedimentological investigations showed that Bays typically have 1.5 m to 2.5 m thick sand rims that show a gradient in texture, with coarser sand at the bottom and finer sand and silt towards the top. Their basins were found to be 0.5 m to 2 m thick and showed a mix of clayey, silty, and sandy deposits. Last, the results compiled during this study were compared to similar depressional features (i.e., playa-lunette systems) to pinpoint any similarities in origin processes. Altogether, this study shows that CNNs are valuable tools for automated geomorphic feature detection and can lead to new insights when coupled with various forms of remotely sensed and field-based datasets.

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Improved Mask R-CNN with distance guided intersection over union for GPR signature detection and segmentation

Cited by 75 publications

References 34 publications

[Retracted] Adoption of Convolutional Neural Network Algorithm Combined with Augmented Reality in Building Data Visualization and Intelligent Detection

[Retracted] Adoption of Convolutional Neural Network Algorithm Combined with Augmented Reality in Building Data Visualization and Intelligent Detection

Real-Time Monitoring of COVID-19 SOP in Public Gathering Using Deep Learning Technique

Using Convolutional Neural Networks for Detection and Morphometric Analysis of Carolina Bays from Publicly Available Digital Elevation Models

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