Single-Stage UAV Detection and Classification with YOLOV5: Mosaic Data Augmentation and PANet

Dadboud, Fardad; Patel, Vaibhav Amit; Mehta, Varun; Bolić, Miodrag; Mantegh, Iraj

doi:10.1109/avss52988.2021.9663841

Cited by 54 publications

(21 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The state-of-the-art comparison of the proposed YOLOv5 and v7 with the schemes mentioned in the literature is shown in Table III. It is evident that mAP of YOLOv5 and v7 has outperformed the work given in [9], [10], [11] and [13]. The proposed YOLOv5 scheme has also performed well in terms of F1 score and yielded the highest value compared to both the YOLOv4 and YOLOv5 existing schemes.…”

Section: A Comparison With State-of-the Artmentioning

confidence: 85%

“…This paper excellently addresses the detection problem, but it can only identify multirotor and helicopter drones; it does not perform well for other UAV types [10]. In [11], researchers solved the problem of drones vs. birds by proposing a visual drone detector based on YOLOV5 and an air-to-air UAV dataset containing small objects and complex backgrounds. They additionally trained a model using faster region convolutional neural network (R-CNN) and feature pyramid networks (FPN) techniques.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning Empowered Fast and Accurate Multiclass UAV Detection in Challenging Weather Conditions

Kaleem¹,

Khan²,

Dil³

et al. 2022

Preprint

View full text Add to dashboard Cite

The emergence of Unmanned Aerial Vehicles (UAVs) raised multiple concerns, given their potentially malicious misuse in unlawful acts. Vision-based counter-UAV applications offer a reliable solution compared to acoustic and radio frequency-based solutions because of their high detection accuracy in diverse weather conditions. The existing solutions work well on trained datasets, but their accuracy is relatively low for real-time detection. In this paper, we model deep learning-empowered solutions to improve the multiclass UAV's classification performance using single-shot object detection algorithms (YOLOv5 and YOLOv7). They efficiently and correctly differentiate between multirotor, fixed-wing, and single-rotor UAVs in challenging weather conditions. Experiments show that the suggested technique is reliable with an overall best average-classification precision of 86.7\%, 88.5\% average recall, 91.8\% average mAP, and 58.4\% average-IoU.

show abstract

Section: A Comparison With State-of-the Artmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Deep Learning Empowered Fast and Accurate Multiclass UAV Detection in Challenging Weather Conditions

Kaleem¹,

Khan²,

Dil³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Therefore, an improved YOLOv5s flame smoke target detection algorithm based on ODConvBS is proposed and improved according to the existing problems. Firstly, in order to improve the generalization of the model, the Mosaic [25] mosaic data enhancement technique and Mixup [26] mixed class data enhancement technique are used to further enrich the data diversity and improve the robustness of the model. Secondly, in a bit to improve the model's speed and accuracy for flame and smoke detection, Omni-dimensional dynamic convolution (ODConv) is added to the backbone network's convolutional block to form a new convolutional block (ODConvBS), which reduces network computation and improves the multi-convolutional kernel fusion model expression capability.…”

Section: Related Workmentioning

confidence: 99%

Flame and Smoke Detection Algorithm Based on ODConvBS-YOLOv5s

Zhang

Xiao

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Real-time and accurate detection of flame and smoke is an important prerequisite to reduce the loss caused by fire. There exists some problems in traditional flame and smoke detection algorithm, such as low accuracy, high miss rate, low detection efficiency and low detection rate of small targets. This paper proposes a YOLOv5s flame smoke detection algorithm based on ODConvBS. Firstly, in the YOLOv5s backbone network, the ordinary convolution block is replaced by ODConvBS to realize the extraction of attention features of the convolution kernel; Secondly, Gnconv is introduced into Neck to improve the model's high-order spatial information extraction ability; then the Shuffle Attention module is added at the end of Neck to facilitate the fusion of different groups of features; At last, in the prediction section, the SIOU loss function, which can account for the angle of the prediction frame vector, is utilized to speed up model convergence. When utilizing the self-made flame and smoke data set, the upgraded YOLOv5s model mAP grew by 9.3% .At the same time, the accuracy rate and the recall rate and the detection speed increased to 83.5%, 83.7%, 33.3FPS respectively.

show abstract

“…An initial approach for drone detection is to adopt state-of-the-art ConvNets, including SSD 19 , Faster R-CNN 42 , Yolov2 43 , Yolov4 20 and more recently Yolov5 44,45 . Because these models are large and complex, synthetic data are required and the real-time constraint is removed.…”

Section: Drone Detectionmentioning

confidence: 99%

Small Flying Object Detection and Tracking in Digital Airport Tower through Spatial-Temporal ConvNets

Thai

Alam

Lilith

et al. 2023

Preprint

View full text Add to dashboard Cite

Digital towers using high-resolution cameras that cover a 360-degree view of airports have recently been applied as a solution for some airports by replacing conventional towers. Although many computer vision systems have been developed as tools to assist tower controllers, small flying object detection remains challenging due to their small dimensions and unpredictable trajectories. This paper proposes a novel computer vision framework to detect, track and recognize small flying objects, namely aircraft and drones, in an airport environment. The framework creates a new Convolutional Neural Network which adapts to the unique characteristics of small flying objects. It also exploits the spatial-temporal information, as well as post-processing, to improve the performance. The proposed framework is validated on an airport dataset and Drone-vs-Bird public dataset. The results show that the framework can not only perform object detection in real-time, but also surpass the performance of state-of-the-art models in both datasets by a large margin.

show abstract

Single-Stage UAV Detection and Classification with YOLOV5: Mosaic Data Augmentation and PANet

Cited by 54 publications

References 23 publications

Deep Learning Empowered Fast and Accurate Multiclass UAV Detection in Challenging Weather Conditions

Deep Learning Empowered Fast and Accurate Multiclass UAV Detection in Challenging Weather Conditions

Flame and Smoke Detection Algorithm Based on ODConvBS-YOLOv5s

Small Flying Object Detection and Tracking in Digital Airport Tower through Spatial-Temporal ConvNets

Contact Info

Product

Resources

About