Real-time Aerial Detection and Reasoning on Embedded-UAVs
- URL: http://arxiv.org/abs/2305.12414v1
- Date: Sun, 21 May 2023 09:43:17 GMT
- Title: Real-time Aerial Detection and Reasoning on Embedded-UAVs
- Authors: Tin Lai
- Abstract summary: We present a unified pipeline architecture for a real-time detection system on an embedded system for UAVs.
This pipeline of networks can exploit the domain-specific knowledge on aerial pedestrian detection and activity recognition.
- Score: 3.0839245814393728
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present a unified pipeline architecture for a real-time detection system
on an embedded system for UAVs. Neural architectures have been the industry
standard for computer vision. However, most existing works focus solely on
concatenating deeper layers to achieve higher accuracy with run-time
performance as the trade-off. This pipeline of networks can exploit the
domain-specific knowledge on aerial pedestrian detection and activity
recognition for the emerging UAV applications of autonomous surveying and
activity reporting. In particular, our pipeline architectures operate in a
time-sensitive manner, have high accuracy in detecting pedestrians from various
aerial orientations, use a novel attention map for multi-activities
recognition, and jointly refine its detection with temporal information.
Numerically, we demonstrate our model's accuracy and fast inference speed on
embedded systems. We empirically deployed our prototype hardware with full live
feeds in a real-world open-field environment.
Related papers
- Automatic UAV-based Airport Pavement Inspection Using Mixed Real and
Virtual Scenarios [3.0874677990361246]
We propose a vision-based approach to automatically identify pavement distress using images captured by UAVs.
The proposed method is based on Deep Learning (DL) to segment defects in the image.
We demonstrate that the use of a mixed dataset composed of synthetic and real training images yields better results when testing the training models in real application scenarios.
arXiv Detail & Related papers (2024-01-11T16:30:07Z) - Burnt area extraction from high-resolution satellite images based on
anomaly detection [1.8843687952462738]
We build upon the framework of Vector Quantized Variational Autoencoder (VQ-VAE) to perform unsupervised burnt area extraction.
We integrate VQ-VAE into an end-to-end framework with an intensive post-processing step using dedicated vegetation, water and brightness indexes.
arXiv Detail & Related papers (2023-08-25T13:25:27Z) - Building Interpretable and Reliable Open Information Retriever for New
Domains Overnight [67.03842581848299]
Information retrieval is a critical component for many down-stream tasks such as open-domain question answering (QA)
We propose an information retrieval pipeline that uses entity/event linking model and query decomposition model to focus more accurately on different information units of the query.
We show that, while being more interpretable and reliable, our proposed pipeline significantly improves passage coverages and denotation accuracies across five IR and QA benchmarks.
arXiv Detail & Related papers (2023-08-09T07:47:17Z) - Deep Learning Computer Vision Algorithms for Real-time UAVs On-board
Camera Image Processing [77.34726150561087]
This paper describes how advanced deep learning based computer vision algorithms are applied to enable real-time on-board sensor processing for small UAVs.
All algorithms have been developed using state-of-the-art image processing methods based on deep neural networks.
arXiv Detail & Related papers (2022-11-02T11:10:42Z) - Virtual Reality via Object Poses and Active Learning: Realizing
Telepresence Robots with Aerial Manipulation Capabilities [39.29763956979895]
This article presents a novel telepresence system for advancing aerial manipulation in dynamic and unstructured environments.
The proposed system not only features a haptic device, but also a virtual reality (VR) interface that provides real-time 3D displays of the robot's workspace.
We show over 70 robust executions of pick-and-place, force application and peg-in-hole tasks with the DLR cable-Suspended Aerial Manipulator (SAM)
arXiv Detail & Related papers (2022-10-18T08:42:30Z) - DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly
detection in air transportation [68.8204255655161]
We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE)
It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase.
Results show that the DAE achieves better results in both accuracy and speed of detection.
arXiv Detail & Related papers (2021-09-08T14:07:55Z) - Robust Object Detection via Instance-Level Temporal Cycle Confusion [89.1027433760578]
We study the effectiveness of auxiliary self-supervised tasks to improve the out-of-distribution generalization of object detectors.
Inspired by the principle of maximum entropy, we introduce a novel self-supervised task, instance-level temporal cycle confusion (CycConf)
For each object, the task is to find the most different object proposals in the adjacent frame in a video and then cycle back to itself for self-supervision.
arXiv Detail & Related papers (2021-04-16T21:35:08Z) - Perceiving Traffic from Aerial Images [86.994032967469]
We propose an object detection method called Butterfly Detector that is tailored to detect objects in aerial images.
We evaluate our Butterfly Detector on two publicly available UAV datasets (UAVDT and VisDrone 2019) and show that it outperforms previous state-of-the-art methods while remaining real-time.
arXiv Detail & Related papers (2020-09-16T11:37:43Z) - Building Robust Industrial Applicable Object Detection Models Using
Transfer Learning and Single Pass Deep Learning Architectures [1.1816942730023883]
We explore how deep convolutional neural networks dedicated to the task of object detection can improve our industrial-oriented object detection pipelines.
By using a deep learning architecture that integrates region proposals, classification and probability estimation in a single run, we aim at obtaining real-time performance.
We apply these algorithms to two industrially relevant applications, one being the detection of promotion boards in eye tracking data and the other detecting and recognizing packages of warehouse products for augmented advertisements.
arXiv Detail & Related papers (2020-07-09T09:50:45Z) - AutoOD: Automated Outlier Detection via Curiosity-guided Search and
Self-imitation Learning [72.99415402575886]
Outlier detection is an important data mining task with numerous practical applications.
We propose AutoOD, an automated outlier detection framework, which aims to search for an optimal neural network model.
Experimental results on various real-world benchmark datasets demonstrate that the deep model identified by AutoOD achieves the best performance.
arXiv Detail & Related papers (2020-06-19T18:57:51Z) - UAV Autonomous Localization using Macro-Features Matching with a CAD
Model [0.0]
This paper presents a novel offline, portable, real-time in-door UAV localization technique that relies on macro-feature detection and matching.
The main contribution of this work is the real-time creation of a macro-feature description vector from the UAV captured images which are simultaneously matched with an offline pre-existing vector from a Computer-Aided Design (CAD) model.
The effectiveness and accuracy of the proposed system were evaluated through simulations and experimental prototype implementation.
arXiv Detail & Related papers (2020-01-30T23:49:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.