scholarly journals Image‐based monitoring of bolt loosening through deep‐learning‐based integrated detection and tracking

2021 ◽  
Author(s):  
Xiao Pan ◽  
T. Y. Yang
Keyword(s):  
Deep Learning ◽  
Detection And Tracking ◽  
Bolt Loosening ◽  
Integrated Detection

scholarly journals Towards Visible and Thermal Drone Monitoring with Convolutional Neural Networks

2019 ◽  
Vol 8 ◽  
Author(s):  
Ye Wang ◽  
Yueru Chen ◽  
Jongmoo Choi ◽  
C.-C. Jay Kuo
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Tracking System ◽  
Synthetic Data ◽  
The Public ◽  
Detection And Tracking ◽  
Individual Module ◽  
Augmentation Techniques ◽  
Bounding Boxes ◽  
Integrated Detection

This paper reports a visible and thermal drone monitoring system that integrates deep-learning-based detection and tracking modules. The biggest challenge in adopting deep learning methods for drone detection is the paucity of training drone images especially thermal drone images. To address this issue, we develop two data augmentation techniques. One is a model-based drone augmentation technique that automatically generates visible drone images with a bounding box label on the drone's location. The other is exploiting an adversarial data augmentation methodology to create thermal drone images. To track a small flying drone, we utilize the residual information between consecutive image frames. Finally, we present an integrated detection and tracking system that outperforms the performance of each individual module containing detection or tracking only. The experiments show that, even being trained on synthetic data, the proposed system performs well on real-world drone images with complex background. The USC drone detection and tracking dataset with user labeled bounding boxes is available to the public.

A unified framework for automated person re-indentification

2020 ◽  
Vol 71 (7) ◽  
pp. 868-880
Author(s):  
Nguyen Hong-Quan ◽  
Nguyen Thuy-Binh ◽  
Tran Duc-Long ◽  
Le Thi-Lan
Keyword(s):  
Deep Learning ◽  
Video Analysis ◽  
Camera Network ◽  
Unified Framework ◽  
Person Detection ◽  
Practical Applications ◽  
Detection And Tracking ◽  
Analysis System ◽  
Bounding Boxes

Along with the strong development of camera networks, a video analysis system has been become more and more popular and has been applied in various practical applications. In this paper, we focus on person re-identification (person ReID) task that is a crucial step of video analysis systems. The purpose of person ReID is to associate multiple images of a given person when moving in a non-overlapping camera network. Many efforts have been made to person ReID. However, most of studies on person ReID only deal with well-alignment bounding boxes which are detected manually and considered as the perfect inputs for person ReID. In fact, when building a fully automated person ReID system the quality of the two previous steps that are person detection and tracking may have a strong effect on the person ReID performance. The contribution of this paper are two-folds. First, a unified framework for person ReID based on deep learning models is proposed. In this framework, the coupling of a deep neural network for person detection and a deep-learning-based tracking method is used. Besides, features extracted from an improved ResNet architecture are proposed for person representation to achieve a higher ReID accuracy. Second, our self-built dataset is introduced and employed for evaluation of all three steps in the fully automated person ReID framework.

A Deep Learning Model for Detection and Tracking in High-Throughput Images of Organoid

2021 ◽  
pp. 104490
Author(s):  
Xuesheng Bian ◽  
Gang Li ◽  
Cheng Wang ◽  
Weiquan Liu ◽  
Xiuhong Lin ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Throughput ◽  
Learning Model ◽  
Detection And Tracking ◽  
Deep Learning Model

Crop stem detection and tracking for precision hoeing using deep learning

2022 ◽  
Vol 192 ◽  
pp. 106606
Author(s):  
Louis Lac ◽  
Jean-Pierre Da Costa ◽  
Marc Donias ◽  
Barna Keresztes ◽  
Alain Bardet
Keyword(s):  
Deep Learning ◽  
Detection And Tracking

UAV Aerial Photography Target Detection and Tracking Based on Deep Learning

2021 ◽  
pp. 426-438
Author(s):  
Xiaohua Li ◽  
Feiyang Wang ◽  
Aiming Xu ◽  
Guofeng Zhang
Keyword(s):  
Deep Learning ◽  
Target Detection ◽  
Aerial Photography ◽  
Detection And Tracking
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