Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3

Benjdira, Bilel; Khursheed, Taha; Koubâa, Anis; Ammar, Adel; Ouni, Kaïs

doi:10.1109/uvs.2019.8658300

Cited by 215 publications

(107 citation statements)

References 17 publications

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“…Two facts are defending this choice. Firstly, YOLOv3 [18] had proven its efficiency compared to other object detection algorithms [9]. Secondly, it has an efficient inference time (up to 45 frames per second).…”

Section: Algorithms Backgroundmentioning

confidence: 99%

“…In this paper, we propose a novel solution to the posture recognition of Salat using convolutional neural networks (CNN), which aims at identifying the four basic postures of Salat namely, Standing (Qiam), Bowing (Ruku), Prostration (Sujud), Sitting (Julus), using state-of-the-art CNN algorithms. CNN has been used in a wide variety of applications such as vehicle detection [9,10], semantic segmentation of urban environments [11], and selfdriving vehicles [12]. The contribution presented in this paper represents the first step towards the main objective of this project that consists in developing an AI-based tool for the assessment of the Islamic prayer and an assistive system that helps beginners and kids to correct the postures during Salat.…”

Section: Introductionmentioning

confidence: 99%

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Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning

Koubâa

Ammar

Benjdira

et al. 2020

2020 6th Conference on Data Science and Machine Learning Applications (CDMA)

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In the Muslim community, the prayer (i.e. Salat) is the second pillar of Islam, and it is the most essential and fundamental worshiping activity that believers have to perform five times a day. From a gestures' perspective, there are predefined human postures that must be performed in a precise manner. However, for several people, these postures are not correctly performed, due to being new to Salat or even having learned prayers in an incorrect manner. Furthermore, the time spent in each posture has to be balanced. To address these issues, we propose to develop an artificial intelligence assistive framework that guides worshippers to evaluate the correctness of the postures of their prayers. This paper represents the first step to achieve this objective and addresses the problem of the recognition of the basic gestures of Islamic prayer using Convolutional Neural Networks (CNN). The contribution of this paper lies in building a dataset for the basic Salat positions, and train a YOLOv3 neural network for the recognition of the gestures. Experimental results demonstrate that the mean average precision attains 85% for a training dataset of 764 images of the different postures. To the best of our knowledge, this is the first work that addresses human activity recognition of Salat using deep learning.

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Section: Algorithms Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning

Koubâa

Ammar

Benjdira

et al. 2020

2020 6th Conference on Data Science and Machine Learning Applications (CDMA)

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“…Autonomous Unmanned Aerial Vehicles (UAVs) are an emerging technology that has attracted several applications such as smart cities, border surveillance , traffic monitoring [1], security, natural disaster monitoring, real-time object tracking [2] and transport [3], [4]. These flying vehicles are controlled either remotely from a Ground Control Station (GCS) or autonomously by a preprogrammed mission.…”

Section: Introductionmentioning

confidence: 99%

MAVSec: Securing the MAVLink Protocol for Ardupilot/PX4 Unmanned Aerial Systems

Allouch

Cheikhrouhou

Koubâa

et al. 2019

2019 15th International Wireless Communications &Amp; Mobile Computing Conference (IWCMC)

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The MAVLink is a lightweight communication protocol between Unmanned Aerial Vehicles (UAVs) and ground control stations (GCSs). It defines a set of bi-directional messages exchanged between a UAV (aka drone) and a ground station. The messages carry out information about the UAV's states and control commands sent from the ground station. However, the MAVLink protocol is not secure and has several vulnerabilities to different attacks that result in critical threats and safety concerns. Very few studies provided solutions to this problem. In this paper, we discuss the security vulnerabilities of the MAVLink protocol and propose MAVSec, a security-integrated mechanism for MAVLink that leverages the use of encryption algorithms to ensure the protection of exchanged MAVLink messages between UAVs and GCSs. To validate MAVSec, we implemented it in Ardupilot and evaluated the performance of different encryption algorithms (i.e. AES-CBC, AES-CTR, RC4 and ChaCha20) in terms of memory usage and CPU consumption. The experimental results show that ChaCha20 has a better performance and is more efficient than other encryption algorithms. Integrating ChaCha20 into MAVLink can guarantee its messages confidentiality, without affecting its performance, while occupying less memory and CPU consumption, thus, preserving memory and saving the battery for the resource-constrained drone.

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“…Recently, there have been several research works that addressed the problem of car detection from aerial images. In our previous work [1], we also compared between YOLOv3 and Faster R-CNN in detecting cars from aerial images. However, we only used one small dataset from low altitude UAV images collected at the premises of Prince Sultan University.…”

Section: Introductionmentioning

confidence: 99%

Aerial Images Processing for Car Detection using Convolutional Neural Networks: Comparison between Faster R-CNN and YoloV3

Ammar¹,

Koubâa²,

Ahmed³

et al. 2019

Preprint

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In this paper, we address the problem of car detection from aerial images using Convolutional Neural Networks (CNN). This problem presents additional challenges as compared to car (or any object) detection from ground images because features of vehicles from aerial images are more difficult to discern. To investigate this issue, we assess the performance of two state-of-the-art CNN algorithms, namely Faster R-CNN, which is the most popular region-based algorithm, and YOLOv3, which is known to be the fastest detection algorithm. We analyze two datasets with different characteristics to check the impact of various factors, such as UAV's altitude, camera resolution, and object size. The objective of this work is to conduct a robust comparison between these two cutting-edge algorithms. By using a variety of metrics, we show that none of the two algorithms outperforms the other in all cases.

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Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3

Cited by 215 publications

References 17 publications

Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning

Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning

MAVSec: Securing the MAVLink Protocol for Ardupilot/PX4 Unmanned Aerial Systems

Aerial Images Processing for Car Detection using Convolutional Neural Networks: Comparison between Faster R-CNN and YoloV3

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