The recall rate of the trained excavator detection model is 99.4%, demonstrating that the trained model has a very high accuracy. Then, the UAV for an excavator detection system (UAV-ED) is
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ResNet [16] is one of the most popular DNN models nowadays and its architecture is based on CNN network. This network was used in various problems of image processing including image recognition
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(SAP) A Self-Adaptive Proposal Model for Temporal Action Detection based on Reinforcement Learning (AAAI 2018) paper code.Torch; 2017 (TCN) Temporal Context Network for Activity Localization in Videos (ICCV 2017) paper code.caffe (SSN) Temporal Action Detection with Structured Segment Networks (ICCV 2017) paper code.PyTorch
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Model architecture overview R-CNN, Faster R-CNN, Mask R-CNN. A number of popular object detection models belong to the R-CNN family. Short for region convolutional neural network, these architectures are based on the region proposal structure discussed above. Over the years, they've become both more accurate and more computationally efficient.
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A widely used deep-learning algorithm, namely You Only Look Once V3, is first used to train the excavator detection model on a workstation and then deployed on an embedded board that is carried by a UAV. The recall rate of the trained excavator detection model is 99.4%, demonstrating that the trained model has a very high accuracy.
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Oct 01, 2021 · To verify the feasibility of the excavator working stage identification based on the control signals of the operating handles and compare the performance of the model on the nonlinear sequence data classification problem, the LSTM, RNN, and LIBSVM classifiers were trained using the sample space established (see Section 3.2.2). All experiments
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Excavator Research Papers - Academia.edu
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Oct 01, 2021 · To verify the feasibility of the excavator working stage identification based on the control signals of the operating handles and compare the performance of the model on the nonlinear sequence data classification problem, the LSTM, RNN, and LIBSVM classifiers were trained using the sample space established (see Section 3.2.2). All experiments
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Remote Sensing | Special Issue : Trends in UAV Remote
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Lane detection is a challenging problem. It has attracted the attention of the computer vision community for several decades. Essentially, lane detection is a multifeature detection problem that has become a real challenge for computer vision and machine learning techniques. Although many machine learning methods are used for lane detection, they are mainly used for classification rather than
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Sep 21, 2017 · XCAVATOR is a collection of perl, bash, R and fortran codes and its computational architecture has been derived from the EXCAVATOR tool that we published in 2013 for the detection of CNVs/sCNA from whole-exome sequencing data. Our tool takes as input WGS data as BAM files and gives as output plots reporting raw, normalized, segmented and called
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The increased adoption of electronic controls in offhighway machines increases the complexity of typical machine systems and stresses the traditional process used to develop these machines. To address this issue design engineers are turning from the traditional design methods to Model-Based Design. By using models in the early design stages, engineers can create executable specifications that
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Oct 14, 2021 · Based on the image captured, three types of images need to be processed for progress monitoring: optical, thermal and camera-view images. Optical and thermal images are taken simultaneously from the construction site by using IR-Cameras. Camera-view images are extracted from the 4D BIM model based on the location and orientation of the camera.
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The result, excavator model using power 12V DC the control system and compressed air drive pneumatic system. The results of testing control system work to properly, the rotary motion of the swing system 360 o and use electric voltage 7,5V will have speed 13,598 rpm, so swing motion from the excavator model similar in general.
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The result, excavator model using power 12V DC the control system and compressed air drive pneumatic system. The results of testing control system work to properly, the rotary motion of the swing system 360 o and use electric voltage 7,5V will have speed 13,598 rpm, so swing motion from the excavator model similar in general.
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Oct 19, 2020 · Based on the object recognition and tracking results, postures of non-rigid entities on construction sites, e.g., workers and excavators, can be represented in the form of a parameterized skeleton model as shown in Fig. 4: (a) for a human body; and (b) for an excavator. For workers, their posture estimation can be an informative indicator of
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Real-Time Excavation Detection at Construction Sites using Deep Learning Bas van Boven 1, Peter van der Putten, Anders Astr om2, Hakim Khala 3, and Aske Plaat1 1 LIACS, Leiden University, The Netherlands [email protected], fp.w.h.van.der.putten, [email protected]
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Dec 19, 2020 · Real Time object detection is a technique of detecting objects from video, there are many proposed network architecture that has been published over the years like we discussed EfficientDet in our previous article, which is already outperformed by YOLOv4, Today we are going to discuss YOLOv5.. YOLO refers to "You Only Look Once" is one of the most versatile and famous object detection models.
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Feb 01, 2020 · The framework contains five main modules: excavator detection, excavator tracking, idling state identification, activity recognition, and productivity analysis. First, a detector is used to identify all the excavators in video frames. The detection results provide two kinds of data, i.e. equipment type and region i.
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Based on the Faster R-CNN AI model, an intelligent object recognition technique, excavators are detected in real-time images transmitted from drones and the excavation site is combined with GIS and Augmented Reality (AR) to monitor the excavator location after overlaying it on the map in real time. For intelligent architecture, Client Part
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