Real-Time Small Drones Detection Based on Pruned YOLOv4

Liu, Hansen; Fan, Kuangang; Ouyang, Qinghua; Li, Na

doi:10.3390/s21103374

Cited by 63 publications

(28 citation statements)

References 23 publications

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“…In this study, the problem of drone detection was investigated using the dataset and the proposed method. According to the studies, the dataset for drone detection is obtained using active and passive sensors [35,36]. In studies related to the detection and recognition of drones using active sensors, the use of radar and LIDAR sensors is discussed [14,35,37].…”

Section: Related Workmentioning

confidence: 99%

“…All the methods used in this study performed well in detecting drones, but YOLOv3 provided the best precision. Researchers have also recently used YOLOv4 [52], a pruned YOLOv4 [36], RetinaNet [36], FCOS [36], and YOLOv3 [36] network in video and image datasets to achieve high accuracy in drone detection. The use of YOLOv4 in the first study provided acceptable drone detection results compared to similar studies and had better accuracy.…”

Section: Related Workmentioning

confidence: 99%

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Detection and Recognition of Drones Based on a Deep Convolutional Neural Network Using Visible Imagery

et al. 2022

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Drones are becoming increasingly popular not only for recreational purposes but also in a variety of applications in engineering, disaster management, logistics, securing airports, and others. In addition to their useful applications, an alarming concern regarding physical infrastructure security, safety, and surveillance at airports has arisen due to the potential of their use in malicious activities. In recent years, there have been many reports of the unauthorized use of various types of drones at airports and the disruption of airline operations. To address this problem, this study proposes a novel deep learning-based method for the efficient detection and recognition of two types of drones and birds. Evaluation of the proposed approach with the prepared image dataset demonstrates better efficiency compared to existing detection systems in the literature. Furthermore, drones are often confused with birds because of their physical and behavioral similarity. The proposed method is not only able to detect the presence or absence of drones in an area but also to recognize and distinguish between two types of drones, as well as distinguish them from birds. The dataset used in this work to train the network consists of 10,000 visible images containing two types of drones as multirotors, helicopters, and also birds. The proposed deep learning method can directly detect and recognize two types of drones and distinguish them from birds with an accuracy of 83%, mAP of 84%, and IoU of 81%. The values of average recall, average accuracy, and average F1-score were also reported as 84%, 83%, and 83%, respectively, in three classes.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Detection and Recognition of Drones Based on a Deep Convolutional Neural Network Using Visible Imagery

et al. 2022

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“…Currently, CNN-based object detection methods can be categorized as one of two frameworks: the two-stage framework (e.g., Faster RCNN [ 6 ], FPN citefpn, etc.) and the one-stage framework (e.g., YOLO [ 5 , 12 , 13 ], SSD [ 7 ], etc.). Faster RCNN [ 6 ], a milestone of the two-stage framework, performs object detection with two stages.…”

Section: Related Workmentioning

confidence: 99%

Centered Multi-Task Generative Adversarial Network for Small Object Detection

Wang

Bai

et al. 2021

Sensors

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Despite the breakthroughs in accuracy and efficiency of object detection using deep neural networks, the performance of small object detection is far from satisfactory. Gaze estimation has developed significantly due to the development of visual sensors. Combining object detection with gaze estimation can significantly improve the performance of small object detection. This paper presents a centered multi-task generative adversarial network (CMTGAN), which combines small object detection and gaze estimation. To achieve this, we propose a generative adversarial network (GAN) capable of image super-resolution and two-stage small object detection. We exploit a generator in CMTGAN for image super-resolution and a discriminator for object detection. We introduce an artificial texture loss into the generator to retain the original feature of small objects. We also use a centered mask in the generator to make the network focus on the central part of images where small objects are more likely to appear in our method. We propose a discriminator with detection loss for two-stage small object detection, which can be adapted to other GANs for object detection. Compared with existing interpolation methods, the super-resolution images generated by CMTGAN are more explicit and contain more information. Experiments show that our method exhibits a better detection performance than mainstream methods.

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“…In [15][16][17][18][19][20][21], the YOLOv4 network was applied to target detection tasks such as agricultural product inspection, industrial safety, and robot vision and achieved good detection results. In [22], a method to detect fires and PPEs to assist in monitoring and evacuation tasks was presented, using deep learning-based YOLOv4 and YOLOv4 tiny algorithms to perform the detection task, using a homemade fire dataset to train the model with a maximum average accuracy (mAP) of 76.86%.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Fire Detection Method for Electric Vehicle Charging Stations Based on Machine Vision

Zhang

Yang

Gao

et al. 2022

WEVJ

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During the charging process of electric vehicles (EV), the circuit inside the charger plug is connected in series, the charger input voltage does not match the rated input voltage, the temperature caused by the severe heating of the charging time is too high for too long, and other factors are very likely to trigger a fire in the vehicle charging pile. In this paper, an improved You Only Look Once v4 (YOLOv4) real-time target detection algorithm based on machine vision is proposed to monitor the site based on existing monitoring equipment, transmit live video information in real-time, expand the monitoring range, and significantly reduce the cost of use. During the experiment, the improved neural network model was trained by a homemade fire video image dataset, and a K-means clustering algorithm iwasintroduced to recalculate the anchor frame size for the specific object of flame; the existing dataset was used to perform multiple divisions by using a tenfold cross-validation algorithm, thus avoiding the selection of chance hyperparameters and models that do not have generalization ability because of special divisions. The experimental results show that the improved algorithm is fast and accurate in detecting large-size flames in real-time and small-size flames at the beginning of a fire, with a detection speed of 43 fps/s, mAP value of 91.53%, and F1 value of 0.91. Compared with YOLOv3 and YOLOv4 models, the improved model is sensitive to detecting different sizes of flames. It can suppress false alarms well in a variety of complex lighting environments. The prediction frame size fits the area where the target is located, the detection accuracy remains stable, and the comprehensive performance of the network model is significantly improved to meet the demand of real-time monitoring. It is significant for developing the EV industry and enhancing emergency response capability.

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Real-Time Small Drones Detection Based on Pruned YOLOv4

Cited by 63 publications

References 23 publications

Detection and Recognition of Drones Based on a Deep Convolutional Neural Network Using Visible Imagery

Detection and Recognition of Drones Based on a Deep Convolutional Neural Network Using Visible Imagery

Centered Multi-Task Generative Adversarial Network for Small Object Detection

Real-Time Fire Detection Method for Electric Vehicle Charging Stations Based on Machine Vision

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