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Deep Learning in Drone Detection
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Deep Learning in Drone Detection

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Aerospace Science and Engineering".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 6027

Special Issue Editor

Prof. Dr. İlhan Aydin

E-Mail Website
Guest Editor
Department of Computer Engineering, Firat University, 23200 Elazig, Turkey
Interests: deep learning; image processing; autonomous drone control; drone-based object detection; signal processing

Special Issue Information

Dear Colleagues,

Unmanned aerial vehicles, also known as drones, have become increasingly popular in recent years, both for hobby uses, such as aerial photography and video shooting, and in many other fields, including agriculture, construction, and land monitoring. They have also been used in search and rescue activities in disasters, fire extinguishing activities, logistics, and cargo transportation. However, drones can have negative uses, such as privacy and air traffic security violations, in addition to the use of drones for terrorism and espionage. For this reason, it is important to detect drones that are a threat at a very early stage. Deep learning is a highly effective technology that offers many different methods for detecting and recognizing drones in the air. In particular, it can analyze various features such as image, signal, and heat traces left by drones while flying.

This Special Issue aims to collect new research highlighting future drone detection and classification technologies using novel deep learning methods. We welcome submissions and contributions that include, but are not limited to, the following topics:

  • New deep learning methods for acoustic-based drone detection and classification;
  • Object-detection-based deep learning methods for drone detection and classification with visible or thermal imaging;
  • Drone detection with deep learning methods using radio-frequency-, radar-, and lidar-based signals;
  • Deep learning methods for multi-mode drone detection and classification;
  • Drone detection with hybrid deep learning algorithms.

Prof. Dr. İlhan Aydin
Guest Editor

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Keywords

  • unmanned aerial vehicles
  • drones
  • deep learning
  • classification
  • object detection
  • surveillance

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Published Papers (2 papers)

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Research

14 pages, 7907 KiB  
Article
GC-YOLOv5s: A Lightweight Detector for UAV Road Crack Detection
by Xinjian Xiang, Haibin Hu, Yi Ding, Yongping Zheng and Shanbao Wu
Appl. Sci. 2023, 13(19), 11030; https://doi.org/10.3390/app131911030 - 7 Oct 2023
Cited by 7 | Viewed by 1384
Abstract
This study proposes a GC-YOLOv5s crack-detection network of UAVs to work out several issues, such as the low efficiency, low detection accuracy caused by shadows, occlusions and low contrast, and influences due to road noise in the classic crack-detection methods in the complicated [...] Read more.
This study proposes a GC-YOLOv5s crack-detection network of UAVs to work out several issues, such as the low efficiency, low detection accuracy caused by shadows, occlusions and low contrast, and influences due to road noise in the classic crack-detection methods in the complicated traffic routes. A Focal-GIOU loss function with a focal loss has been introduced in this proposed algorithm, which is applied to address the issue of the imbalance of difficult and easy samples in crack images. Meanwhile, the original localization loss function CIOU is replaced by a GIOU loss function that is more suitable for irregular target (crack) detection. In order to improve the ability of the modified model of representing the features, a Transposed Convolution layer is simultaneously added in place of the original model’s upsampling layer. According to the advantage of computing resources of the Ghost module, the C3Ghost module is applied to decrease the amount of network parameters while maintaining adequate feature representation. Additionally, a lightweight module, CSPCM, is designed with the Conmix module and the Ghost concept, which successfully reduces the model parameters and zooms out the volume. At the same time, this modified module can have enough detection accuracy, and it can satisfy the requirements of UAV detection of small models and rapidity. In order to prove the model’s performance, this study has established a new UAV road-crack-detection dataset (named the UMSC), and has conducted extensive trials. To sum up, the precision of GC-YOLOv5s has increased by 8.2%, 2.8%, and 3.1%, respectively, and has reduced the model parameters by 16.2% in comparison to YOLOv5s. Furthermore, it outperforms previous YOLO comparison models in Precision, Recall, mAP_0.5, mAP_0.5:0.95, and Params. Full article
(This article belongs to the Special Issue Deep Learning in Drone Detection)
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24 pages, 100818 KiB  
Article
WAID: A Large-Scale Dataset for Wildlife Detection with Drones
by Chao Mou, Tengfei Liu, Chengcheng Zhu and Xiaohui Cui
Appl. Sci. 2023, 13(18), 10397; https://doi.org/10.3390/app131810397 - 17 Sep 2023
Cited by 8 | Viewed by 4231
Abstract
Drones are widely used for wildlife monitoring. Deep learning algorithms are key to the success of monitoring wildlife with drones, although they face the problem of detecting small targets. To solve this problem, we have introduced the SE-YOLO model, which incorporates a channel [...] Read more.
Drones are widely used for wildlife monitoring. Deep learning algorithms are key to the success of monitoring wildlife with drones, although they face the problem of detecting small targets. To solve this problem, we have introduced the SE-YOLO model, which incorporates a channel self-attention mechanism into the advanced real-time object detection algorithm YOLOv7, enabling the model to perform effectively on small targets. However, there is another barrier; the lack of publicly available UAV wildlife aerial datasets hampers research on UAV wildlife monitoring algorithms. To fill this gap, we present a large-scale, multi-class, high-quality dataset called WAID (Wildlife Aerial Images from Drone), which contains 14,375 UAV aerial images from different environmental conditions, covering six wildlife species and multiple habitat types. We conducted a statistical analysis experiment, an algorithm detection comparison experiment, and a dataset generalization experiment. The statistical analysis experiment demonstrated the dataset characteristics both quantitatively and intuitively. The comparison and generalization experiments compared different types of advanced algorithms as well as the SE-YOLO method from the perspective of the practical application of UAVs for wildlife monitoring. The experimental results show that WAID is suitable for the study of wildlife monitoring algorithms for UAVs, and SE-YOLO is the most effective in this scenario, with a mAP of up to 0.983. This study brings new methods, data, and inspiration to the field of wildlife monitoring by UAVs. Full article
(This article belongs to the Special Issue Deep Learning in Drone Detection)
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