Drone navigation and license place detection for vehicle location in
indoor spaces
- URL: http://arxiv.org/abs/2307.10165v2
- Date: Thu, 20 Jul 2023 08:53:13 GMT
- Title: Drone navigation and license place detection for vehicle location in
indoor spaces
- Authors: Moa Arvidsson, Sithichot Sawirot, Cristofer Englund, Fernando
Alonso-Fernandez, Martin Torstensson, Boris Duran
- Abstract summary: This work is aimed at creating a solution based on a nano-drone that navigates across rows of parked vehicles and detects their license plates.
All computations are done in real-time on the drone, which just sends position and detected images that allow the creation of a 2D map.
- Score: 55.66423065924684
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Millions of vehicles are transported every year, tightly parked in vessels or
boats. To reduce the risks of associated safety issues like fires, knowing the
location of vehicles is essential, since different vehicles may need different
mitigation measures, e.g. electric cars. This work is aimed at creating a
solution based on a nano-drone that navigates across rows of parked vehicles
and detects their license plates. We do so via a wall-following algorithm, and
a CNN trained to detect license plates. All computations are done in real-time
on the drone, which just sends position and detected images that allow the
creation of a 2D map with the position of the plates. Our solution is capable
of reading all plates across eight test cases (with several rows of plates,
different drone speeds, or low light) by aggregation of measurements across
several drone journeys.
Related papers
- A Real-Time Wrong-Way Vehicle Detection Based on YOLO and Centroid
Tracking [0.0]
Wrong-way driving is one of the main causes of road accidents and traffic jam all over the world.
In this paper, we propose an automatic wrong-way vehicle detection system from on-road surveillance camera footage.
arXiv Detail & Related papers (2022-10-19T00:53:28Z) - Real-time smart vehicle surveillance system [0.0]
Vehicle theft is one of the least solved offenses in India.
We propose a real-time vehicle surveillance system, which detects and tracks the suspect vehicle using the CCTV video feed.
Various image processing and deep learning algorithms are employed to meet the objectives of the proposed system.
arXiv Detail & Related papers (2021-11-24T06:15:14Z) - A dataset for multi-sensor drone detection [67.75999072448555]
The use of small and remotely controlled unmanned aerial vehicles (UAVs) has increased in recent years.
Most studies on drone detection fail to specify the type of acquisition device, the drone type, the detection range, or the dataset.
We contribute with an annotated multi-sensor database for drone detection that includes infrared and visible videos and audio files.
arXiv Detail & Related papers (2021-11-02T20:52:03Z) - Dogfight: Detecting Drones from Drones Videos [58.158988162743825]
This paper attempts to address the problem of drones detection from other flying drones variations.
The erratic movement of the source and target drones, small size, arbitrary shape, large intensity, and occlusion make this problem quite challenging.
To handle this, instead of using region-proposal based methods, we propose to use a two-stage segmentation-based approach.
arXiv Detail & Related papers (2021-03-31T17:43:31Z) - Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object
Detection [55.12894776039135]
State-of-the-art 3D object detectors, based on deep learning, have shown promising accuracy but are prone to over-fit to domain idiosyncrasies.
We propose a novel learning approach that drastically reduces this gap by fine-tuning the detector on pseudo-labels in the target domain.
We show, on five autonomous driving datasets, that fine-tuning the detector on these pseudo-labels substantially reduces the domain gap to new driving environments.
arXiv Detail & Related papers (2021-03-26T01:18:11Z) - End-to-end trainable network for degraded license plate detection via
vehicle-plate relation mining [26.484883058620134]
We propose a novel and applicable method for degraded license plate detection via vehicle-plate relation mining.
First, we estimate the local region around the license plate by using the relationships between the vehicle and the license plate.
Second, we propose to predict the quadrilateral bounding box in the local region by regressing the four corners of the license plate to robustly detect oblique license plates.
arXiv Detail & Related papers (2020-10-27T13:05:31Z) - Deep Learning Based Traffic Surveillance System For Missing and
Suspicious Car Detection [0.0]
This paper presents a deep learning based automatic traffic surveillance system for the detection of stolen/suspicious cars.
It mainly comprises of four parts: Select-Detector, Image Quality Enhancer, Image Transformer, and Smart Recognizer.
The effectiveness of the proposed approach is tested on the government's CCTV camera footage, which resulted in identifying the stolen/suspicious cars with an accuracy of 87%.
arXiv Detail & Related papers (2020-07-17T07:18:12Z) - Depth Sensing Beyond LiDAR Range [84.19507822574568]
We propose a novel three-camera system that utilizes small field of view cameras.
Our system, along with our novel algorithm for computing metric depth, does not require full pre-calibration.
It can output dense depth maps with practically acceptable accuracy for scenes and objects at long distances.
arXiv Detail & Related papers (2020-04-07T00:09:51Z) - Physically Realizable Adversarial Examples for LiDAR Object Detection [72.0017682322147]
We present a method to generate universal 3D adversarial objects to fool LiDAR detectors.
In particular, we demonstrate that placing an adversarial object on the rooftop of any target vehicle to hide the vehicle entirely from LiDAR detectors with a success rate of 80%.
This is one step closer towards safer self-driving under unseen conditions from limited training data.
arXiv Detail & Related papers (2020-04-01T16:11:04Z)
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.