DiagonalNet: Confidence diagonal lines for the UAV detection

Hu, Qintao; Duan, Qianwen; Mao, Yao; Zhou, Xi; Zhou, Guomei

doi:10.1002/tee.22938

Cited by 2 publications

(2 citation statements)

References 32 publications

(45 reference statements)

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“…26 Hu et al replaced the feature extraction network of DiafonalNet with the improved Hourglassnet, which made the detection accuracy higher, but the disadvantage was that there was no further test for speed. 27 Seidaliyeva et al detected moving objects in the static background, then they classified the moving objects to further improve the accuracy of the model, but the disadvantage is that the model had poor real-time performance. 28 Makirin used a web application for real-time detection when he participated in the UAV Chasing Challenge.…”

Section: Related Workmentioning

confidence: 99%

AD-YOLOv5s based UAV detection for low altitude security

Shang

Liu

Qiu

et al. 2023

International Journal of Micro Air Vehicles

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UAV (Unmanned Aerial Vehicle) black flight at low altitude could cause serious safety risks. Consequently, it is crucial to detect and manage low altitude small UAVs. The existing methods of low altitude small UAV detection suffer from problems such as high false alarm rate, and poor real-time performance. In order to solve the above problems, we present a novel approach, named AD-YOLOv5s, to achieve low altitude small UAV detection with high precision and high real-time performance. Firstly, the feature enhancement method is used to expand the dataset. We optimize the model feature fusion, the prediction head structure, and the loss function. Based on the CBAM (Convolutional Block Attention Module) attention mechanism, feature enhancement is performed to improve the detection accuracy. Secondly, the ghost module and depthwise separable convolution are used to reduce the number of parameters of the model, and we propose the method of lightweight design of model to improve the detection speed. Compared with the YOLOv5s model, the experiment result shows that our proposed AD-YOLOv5s model improves the value of mAP by 2.2% and the value of Recall by 1.8%, reduces the value of GFLOPs by 29.9% and parameters by 38.8%, and achieves 27.6 FPS when the proposed model deploy on a low-cost edge computing device (jetson nano).

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Section: Related Workmentioning

confidence: 99%

AD-YOLOv5s based UAV detection for low altitude security

Shang

Liu

Qiu

et al. 2023

International Journal of Micro Air Vehicles

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“…Hu et al (22) proposed an object detection method, called DiagonalNet, by using an improved hourglass CNN as its backbone network and generating confidence diagonal lines as the detection result. A large dataset (10,974 sample images) was created by processing videos and photos with different backgrounds and lighting, augmented by rotating and flipping each image with random angles.…”

Section: Drone Detection Modelsmentioning

confidence: 99%

Improving real-time drone detection for counter-drone systems

2021

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The number of unmanned aerial vehicles (UAVs, also known as drones) has increased dramatically in the airspace worldwide for tasks such as surveillance, reconnaissance, shipping and delivery. However, a small number of them, acting maliciously, can raise many security risks. Recent Artificial Intelligence (AI) capabilities for object detection can be very useful for the identification and classification of drones flying in the airspace and, in particular, are a good solution against malicious drones. A number of counter-drone solutions are being developed, but the cost of drone detection ground systems can also be very high, depending on the number of sensors deployed and powerful fusion algorithms. We propose a low-cost counter-drone solution composed uniquely by a guard-drone that should be able to detect, locate and eliminate any malicious drone. In this paper, a state-of-the-art object detection algorithm is used to train the system to detect drones. Three existing object detection models are improved by transfer learning and tested for real-time drone detection. Training is done with a new dataset of drone images, constructed automatically from a very realistic flight simulator. While flying, the guard-drone captures random images of the area, while at the same time, a malicious drone is flying too. The drone images are auto-labelled using the location and attitude information available in the simulator for both drones. The world coordinates for the malicious drone position must then be projected into image pixel coordinates. The training and test results show a minimum accuracy improvement of 22% with respect to state-of-the-art object detection models, representing promising results that enable a step towards the construction of a fully autonomous counter-drone system.

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DiagonalNet: Confidence diagonal lines for the UAV detection

Cited by 2 publications

References 32 publications

AD-YOLOv5s based UAV detection for low altitude security

AD-YOLOv5s based UAV detection for low altitude security

Improving real-time drone detection for counter-drone systems

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