An improved YOLO-based road traffic monitoring system

Al-qaness, Mohammed A. A.; Abbasi, Aaqif Afzaal; Fan, Hong; Ibrahim, Rehab Ali; Alsamhi, Saeed Hamood; Hawbani, Ammar

doi:10.1007/s00607-020-00869-8

Cited by 54 publications

(24 citation statements)

References 26 publications

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“…CNN’s are commonly used in computer vision problems. However, CNN’s can be extended and employed in research fields tackling natural language processing [ 39 , 40 , 41 ], image processing [ 42 , 43 ], green computing [ 44 , 45 ], remote sensing [ 46 , 47 ], and others [ 48 ]. Unlike traditional machine learning algorithms that rely on handcrafted feature extraction, CNNs can automatically learn and represent complex features.…”

Section: Proposed Modelmentioning

confidence: 99%

Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System

Fatani

Dahou

Al-qaness

et al. 2021

Sensors

Self Cite

103

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Developing cyber security is very necessary and has attracted considerable attention from academy and industry organizations worldwide. It is also very necessary to provide sustainable computing for the the Internet of Things (IoT). Machine learning techniques play a vital role in the cybersecurity of the IoT for intrusion detection and malicious identification. Thus, in this study, we develop new feature extraction and selection methods and for the IDS system using the advantages of the swarm intelligence (SI) algorithms. We design a feature extraction mechanism depending on the conventional neural networks (CNN). After that, we present an alternative feature selection (FS) approach using the recently developed SI algorithm, Aquila optimizer (AQU). Moreover, to assess the quality of the developed IDS approach, four well-known public datasets, CIC2017, NSL-KDD, BoT-IoT, and KDD99, were used. We also considered extensive comparisons to other optimization methods to verify the competitive performance of the developed method. The results show the high performance of the developed approach using different evaluation indicators.

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Section: Proposed Modelmentioning

confidence: 99%

Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System

Fatani

Dahou

Al-qaness

et al. 2021

Sensors

Self Cite

103

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“…Similarly, Mohammed A. A. Al-qaness [43] has proposed a vehicle tracking system based on intelligent video surveillance. To monitor automobiles, the suggested system employs a neural network, image-based tracking, and YOLOv3 (You Only Look Once).…”

Section: Object Detection and Counting Using Deep Learning Techniquesmentioning

confidence: 99%

“…We compared our proposed object counting model with two similar approach models from the existing literature [5,43]. The referred approaches produced a counting accuracy of 86.7% and 85% respectively.…”

Section: Performance Comparison With Other Related Workmentioning

confidence: 99%

An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments

et al. 2021

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Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the literature to address this problem. However, the resulting detection accuracy is still not adequate. This paper proposes an efficient approach that uses deep learning concepts and correlation filters for multi-object counting and tracking. The performance of the proposed system is evaluated using a dataset consisting of 16 videos with different features to examine the impact of object density, image quality, angle of view, and speed of motion towards system accuracy. Performance evaluation exhibits promising results in normal traffic scenarios and adverse weather conditions. Moreover, the proposed approach outperforms the performance of two recent approaches from the literature.

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“…In this short communication, we propose a feasible solution for heavy goods vehicle detection. Computer Vision algorithms have been implemented for various tasks in traffic monitoring for many years, e.g., traffic sign recognition [1][2][3][4][5][6][7]; intelligent traffic light system [8]; vehicle speed monitoring [9]; traffic violation monitoring [10]; vehicle tracking [11][12][13]; vehicle classification [14][15][16][17][18][19][20][21][22][23][24][25][26]; vehicle counting system on streets and highways [27][28][29][30][31]; parking spot detection from the point of view of the car for parking assistants [32,33]; and parking spot monitoring [34][35][36][37][38][39][40][41][42][43][44][45][46][47]…”

Section: Introductionmentioning

confidence: 99%

Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5

et al. 2021

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The proper planning of rest periods in response to the availability of parking spaces at rest areas is an important issue for haulage companies as well as traffic and road administrations. We present a case study of how You Only Look Once (YOLO)v5 can be implemented to detect heavy goods vehicles at rest areas during winter to allow for the real-time prediction of parking spot occupancy. Snowy conditions and the polar night in winter typically pose some challenges for image recognition, hence we use thermal network cameras. As these images typically have a high number of overlaps and cut-offs of vehicles, we applied transfer learning to YOLOv5 to investigate whether the front cabin and the rear are suitable features for heavy goods vehicle recognition. Our results show that the trained algorithm can detect the front cabin of heavy goods vehicles with high confidence, while detecting the rear seems more difficult, especially when located far away from the camera. In conclusion, we firstly show an improvement in detecting heavy goods vehicles using their front and rear instead of the whole vehicle, when winter conditions result in challenging images with a high number of overlaps and cut-offs, and secondly, we show thermal network imaging to be promising in vehicle detection.

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An improved YOLO-based road traffic monitoring system

Cited by 54 publications

References 26 publications

Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System

Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System

An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments

Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5

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