158 research outputs found
Effect of Annotation Errors on Drone Detection with YOLOv3
Following the recent advances in deep networks, object detection and tracking
algorithms with deep learning backbones have been improved significantly;
however, this rapid development resulted in the necessity of large amounts of
annotated labels. Even if the details of such semi-automatic annotation
processes for most of these datasets are not known precisely, especially for
the video annotations, some automated labeling processes are usually employed.
Unfortunately, such approaches might result with erroneous annotations. In this
work, different types of annotation errors for object detection problem are
simulated and the performance of a popular state-of-the-art object detector,
YOLOv3, with erroneous annotations during training and testing stages is
examined. Moreover, some inevitable annotation errors in CVPR-2020 Anti-UAV
Challenge dataset is also examined in this manner, while proposing a solution
to correct such annotation errors of this valuable data set.Comment: Best Paper Award at The 1st Anti-UAV Workshop & Challenge - CVPR
Workshops, 202
SeeCucumbers: using deep learning and drone iagery to detect sea cucumbers on coral reef flats
Sea cucumbers (Holothuroidea or holothurians) are a valuable fishery and are also crucial nutrient recyclers, bioturbation agents, and hosts for many biotic associates. Their ecological impacts could be substantial given their high abundance in some reef locations and thus monitoring their populations and spatial distribution is of research interest. Traditional in situ surveys are laborious and only cover small areas but drones offer an opportunity to scale observations more broadly, especially if the holothurians can be automatically detected in drone imagery using deep learning algorithms. We adapted the object detection algorithm YOLOv3 to detect holothurians from drone imagery at Hideaway Bay, Queensland, Australia. We successfully detected 11,462 of 12,956 individuals over 2.7ha with an average density of 0.5 individual/m2. We tested a range of hyperparameters to determine the optimal detector performance and achieved 0.855 mAP, 0.82 precision, 0.83 recall, and 0.82 F1 score. We found as few as ten labelled drone images was sufficient to train an acceptable detection model (0.799 mAP). Our results illustrate the potential of using small, affordable drones with direct implementation of open-source object detection models to survey holothurians and other shallow water sessile species
YOLO-Drone:Airborne real-time detection of dense small objects from high-altitude perspective
Unmanned Aerial Vehicles (UAVs), specifically drones equipped with remote
sensing object detection technology, have rapidly gained a broad spectrum of
applications and emerged as one of the primary research focuses in the field of
computer vision. Although UAV remote sensing systems have the ability to detect
various objects, small-scale objects can be challenging to detect reliably due
to factors such as object size, image degradation, and real-time limitations.
To tackle these issues, a real-time object detection algorithm (YOLO-Drone) is
proposed and applied to two new UAV platforms as well as a specific light
source (silicon-based golden LED). YOLO-Drone presents several novelties: 1)
including a new backbone Darknet59; 2) a new complex feature aggregation module
MSPP-FPN that incorporated one spatial pyramid pooling and three atrous spatial
pyramid pooling modules; 3) and the use of Generalized Intersection over Union
(GIoU) as the loss function. To evaluate performance, two benchmark datasets,
UAVDT and VisDrone, along with one homemade dataset acquired at night under
silicon-based golden LEDs, are utilized. The experimental results show that, in
both UAVDT and VisDrone, the proposed YOLO-Drone outperforms state-of-the-art
(SOTA) object detection methods by improving the mAP of 10.13% and 8.59%,
respectively. With regards to UAVDT, the YOLO-Drone exhibits both high
real-time inference speed of 53 FPS and a maximum mAP of 34.04%. Notably,
YOLO-Drone achieves high performance under the silicon-based golden LEDs, with
a mAP of up to 87.71%, surpassing the performance of YOLO series under ordinary
light sources. To conclude, the proposed YOLO-Drone is a highly effective
solution for object detection in UAV applications, particularly for night
detection tasks where silicon-based golden light LED technology exhibits
significant superiority
Autonomous High-Precision Landing on a Unmanned Surface Vehicle
THE MAIN GOAL OF THIS THESIS IS THE DEVELOPMENT OF AN AUTONOMOUS
HIGH-PRECISION LANDING SYSTEM OF AN UAV IN AN AUTONOMOUS BOATIn this dissertation, a collaborative method for Multi Rotor Vertical Takeoff and Landing
(MR-VTOL) Unmanned Aerial Vehicle (UAV)s’ autonomous landing is presented. The
majority of common UAV autonomous landing systems adopt an approach in which the
UAV scans the landing zone for a predetermined pattern, establishes relative positions,
and uses those positions to execute the landing. These techniques have some shortcomings,
such as extensive processing being carried out by the UAV itself and requires a lot
of computational power. The fact that most of these techniques only work while the UAV
is already flying at a low altitude, since the pattern’s elements must be plainly visible to
the UAV’s camera, creates an additional issue. An RGB camera that is positioned in the
landing zone and pointed up at the sky is the foundation of the methodology described
throughout this dissertation. Convolutional Neural Networks and Inverse Kinematics
approaches can be used to isolate and analyse the distinctive motion patterns the UAV
presents because the sky is a very static and homogeneous environment. Following realtime
visual analysis, a terrestrial or maritime robotic system can transmit orders to the
UAV.
The ultimate result is a model-free technique, or one that is not based on established
patterns, that can help the UAV perform its landing manoeuvre. The method is trustworthy
enough to be used independently or in conjunction with more established techniques
to create a system that is more robust. The object detection neural network approach was
able to detect the UAV in 91,57% of the assessed frames with a tracking error under 8%,
according to experimental simulation findings derived from a dataset comprising three
different films. Also created was a high-level position relative control system that makes
use of the idea of an approach zone to the helipad. Every potential three-dimensional
point within the zone corresponds to a UAV velocity command with a certain orientation
and magnitude. The control system worked flawlessly to conduct the UAV’s landing
within 6 cm of the target during testing in a simulated setting.Nesta dissertação, é apresentado um método de colaboração para a aterragem autónoma
de Unmanned Aerial Vehicle (UAV)Multi Rotor Vertical Takeoff and Landing (MR-VTOL).
A maioria dos sistemas de aterragem autónoma de UAV comuns adopta uma abordagem
em que o UAV varre a zona de aterragem em busca de um padrão pré-determinado, estabelece
posições relativas, e utiliza essas posições para executar a aterragem. Estas técnicas
têm algumas deficiências, tais como o processamento extensivo a ser efectuado pelo próprio
UAV e requer muita potência computacional. O facto de a maioria destas técnicas só
funcionar enquanto o UAV já está a voar a baixa altitude, uma vez que os elementos do
padrão devem ser claramente visíveis para a câmara do UAV, cria um problema adicional.
Uma câmara RGB posicionada na zona de aterragem e apontada para o céu é a base da
metodologia descrita ao longo desta dissertação. As Redes Neurais Convolucionais e as
abordagens da Cinemática Inversa podem ser utilizadas para isolar e analisar os padrões
de movimento distintos que o UAV apresenta, porque o céu é um ambiente muito estático
e homogéneo. Após análise visual em tempo real, um sistema robótico terrestre ou
marítimo pode transmitir ordens para o UAV.
O resultado final é uma técnica sem modelo, ou que não se baseia em padrões estabelecidos,
que pode ajudar o UAV a realizar a sua manobra de aterragem. O método é
suficientemente fiável para ser utilizado independentemente ou em conjunto com técnicas
mais estabelecidas para criar um sistema que seja mais robusto. A abordagem da rede
neural de detecção de objectos foi capaz de detectar o UAV em 91,57% dos fotogramas
avaliados com um erro de rastreio inferior a 8%, de acordo com resultados de simulação
experimental derivados de um conjunto de dados composto por três filmes diferentes.
Também foi criado um sistema de controlo relativo de posição de alto nível que faz uso
da ideia de uma zona de aproximação ao heliporto. Cada ponto tridimensional potencial
dentro da zona corresponde a um comando de velocidade do UAV com uma certa orientação
e magnitude. O sistema de controlo funcionou sem falhas para conduzir a aterragem
do UAV dentro de 6 cm do alvo durante os testes num cenário simulado.
Traduzido com a versão gratuita do tradutor - www.DeepL.com/Translato
Deep learning classification applied to traffic accidents prediction
[Abstract] In this paper, YOLOv4 neural networks are trained with the goal of detecting and classifying objects from a street as seen from a drone. These have been trained on the VisDrone dataset, which is firstly validated through a custom-made graphic user interface. Then, several tests regarding performance, dataset composition and contrast have been carried out on the trained models. Results are compared to those from other previously existing models in order to evaluate their performance and analyse their shortcomings. The trained models are then used to detect and classify objects in a city scenario in real-time. Finally, an algorithm is proposed to track the objects, infer their future trajectories and predict potential collisions from the expected trajectories.This work has been co-financed by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project SaCoAV (ref. MINECO PID2020-114244RB-I00 ), by the European Regional Development Fund of the European Union in the framework of the ERDF Operational Program of Catalonia 2014-2020 (ref. 001-P-001643 Looming Factory) and by the DGR of Generalitat de Catalunya (SAC group ref. 2017/SGR/482).Generalitat de Catalunya; 2017/SGR/482Generalitat De Catalunya; 001-P-00164
Impact of Noisy Labels on Dental Deep Learning—Calculus Detection on Bitewing Radiographs
Supervised deep learning requires labelled data. On medical images, data is often labelled inconsistently (e.g., too large) with varying accuracies. We aimed to assess the impact of such label noise on dental calculus detection on bitewing radiographs. On 2584 bitewings calculus was accurately labeled using bounding boxes (BBs) and artificially increased and decreased stepwise, resulting in 30 consistently and 9 inconsistently noisy datasets. An object detection network (YOLOv5) was trained on each dataset and evaluated on noisy and accurate test data. Training on accurately labeled data yielded an mAP50: 0.77 (SD: 0.01). When trained on consistently too small BBs model performance significantly decreased on accurate and noisy test data. Model performance trained on consistently too large BBs decreased immediately on accurate test data (e.g., 200% BBs: mAP50: 0.24; SD: 0.05; p < 0.05), but only after drastically increasing BBs on noisy test data (e.g., 70,000%: mAP50: 0.75; SD: 0.01; p < 0.05). Models trained on inconsistent BB sizes showed a significant decrease of performance when deviating 20% or more from the original when tested on noisy data (mAP50: 0.74; SD: 0.02; p < 0.05), or 30% or more when tested on accurate data (mAP50: 0.76; SD: 0.01; p < 0.05). In conclusion, accurate predictions need accurate labeled data in the training process. Testing on noisy data may disguise the effects of noisy training data. Researchers should be aware of the relevance of accurately annotated data, especially when testing model performances
A review of deep learning techniques for detecting animals in aerial and satellite images
Deep learning is an effective machine learning method that in recent years has been successfully applied to detect and monitor species population in remotely sensed data. This study aims to provide a systematic literature review of current applications of deep learning methods for animal detection in aerial and satellite images. We categorized methods in collated publications into image level, point level, bounding-box level, instance segmentation level, and specific information level. The statistical results show that YOLO, Faster R-CNN, U-Net and ResNet are the most used neural network structures. The main challenges associated with the use of these deep learning methods are imbalanced datasets, small samples, small objects, image annotation methods, image background, animal counting, model accuracy assessment, and uncertainty estimation. We explored possible solutions include the selection of sample annotation methods, optimizing positive or negative samples, using weakly and self- supervised learning methods, selecting or developing more suitable network structures. Future research trends we identified are video-based detection, very high-resolution satellite image-based detection, multiple species detection, new annotation methods, and the development of specialized network structures and large foundation models. We discussed existing research attempts as well as personal perspectives on these possible solutions and future trends
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