Shuja Ali¹, Ahmad Jalal¹, Mohammed Hamad Alatiyyah², Khaled Alnowaiser³, Jeongmin Park^4,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1249-1265, 2023, DOI:10.32604/cmc.2023.038114 - 08 June 2023

Abstract Unmanned aerial vehicles (UAVs) can be used to monitor traffic in a variety of settings, including security, traffic surveillance, and traffic control. Numerous academics have been drawn to this topic because of the challenges and the large variety of applications. This paper proposes a new and efficient vehicle detection and tracking system that is based on road extraction and identifying objects on it. It is inspired by existing detection systems that comprise stationary data collectors such as induction loops and stationary cameras that have a limited field of view and are not mobile. The goal… More >

Yingkun Song¹, Shunhe Hong¹, Chentao Hu¹, Pingan He², Lingbing Tao¹, Zhixin Tie^1,3,*, Chengfu Ding⁴

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5761-5784, 2023, DOI:10.32604/cmc.2023.038910 - 29 April 2023

Abstract Rapid and precise vehicle recognition and classification are essential for intelligent transportation systems, and road target detection is one of the most difficult tasks in the field of computer vision. The challenge in real-time road target detection is the ability to properly pinpoint relatively small vehicles in complicated environments. However, because road targets are prone to complicated backgrounds and sparse features, it is challenging to detect and identify vehicle kinds fast and reliably. We suggest a new vehicle detection model called MEB-YOLO, which combines Mosaic and MixUp data augmentation, Efficient Channel Attention (ECA) attention mechanism,… More >

Peicheng Shi^1,*, Heng Qi¹, Zhiqiang Liu¹, Aixi Yang²

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2007-2023, 2023, DOI:10.32604/cmes.2023.022304 - 23 November 2022

Abstract 3D vehicle detection based on LiDAR-camera fusion is becoming an emerging research topic in autonomous driving. The algorithm based on the Camera-LiDAR object candidate fusion method (CLOCs) is currently considered to be a more effective decision-level fusion algorithm, but it does not fully utilize the extracted features of 3D and 2D. Therefore, we proposed a 3D vehicle detection algorithm based on multimodal decision-level fusion. First, project the anchor point of the 3D detection bounding box into the 2D image, calculate the distance between 2D and 3D anchor points, and use this distance as a new… More > Graphic Abstract

Saeed Masoud Alshahrani¹, Saud S. Alotaibi², Shaha Al-Otaibi³, Mohamed Mousa⁴, Anwer Mustafa Hilal^5,*, Amgad Atta Abdelmageed⁵, Abdelwahed Motwakel⁵, Mohamed I. Eldesouki⁶

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3117-3131, 2023, DOI:10.32604/cmc.2023.033038 - 31 October 2022

Abstract Object detection (OD) in remote sensing images (RSI) acts as a vital part in numerous civilian and military application areas, like urban planning, geographic information system (GIS), and search and rescue functions. Vehicle recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition regions. The latest advancements in deep learning (DL) approaches permit the design of effectual OD approaches. This study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection (AEODCNN-VD) model on Remote Sensing Images. The proposed AEODCNN-VD model focuses on… More >

Sunil Kumar¹, Manisha Jailia¹, Sudeep Varshney², Nitish Pathak³, Shabana Urooj^4,*, Nouf Abd Elmunim⁴

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3561-3577, 2023, DOI:10.32604/cmc.2023.029999 - 31 October 2022

Abstract Vehicle detection is still challenging for intelligent transportation systems (ITS) to achieve satisfactory performance. The existing methods based on one stage and two-stage have intrinsic weakness in obtaining high vehicle detection performance. Due to advancements in detection technology, deep learning-based methods for vehicle detection have become more popular because of their higher detection accuracy and speed than the existing algorithms. This paper presents a robust vehicle detection technique based on Improved You Look Only Once (RVD-YOLOv5) to enhance vehicle detection accuracy. The proposed method works in three phases; in the first phase, the K-means algorithm… More >

Ayesha¹, Muhammad Javed Iqbal¹, Iftikhar Ahmad^2,*, Madini O. Alassafi², Ahmed S. Alfakeeh², Ahmed Alhomoud³

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3647-3661, 2023, DOI:10.32604/cmc.2023.020916 - 31 October 2022

Abstract Contemporarily numerous analysts labored in the field of Vehicle detection which improves Intelligent Transport System (ITS) and reduces road accidents. The major obstacles in automatic detection of tiny vehicles are due to occlusion, environmental conditions, illumination, view angles and variation in size of objects. This research centers on tiny and partially occluded vehicle detection and identification in challenging scene specifically in crowed area. In this paper we present comprehensive methodology of tiny vehicle detection using Deep Neural Networks (DNN) namely CenterNet. Substantially DNN disregards objects that are small in size 5 pixels and more false… More >

Sandeep Kumar¹, Arpit Jain^2,*, Shilpa Rani³, Hammam Alshazly⁴, Sahar Ahmed Idris⁵, Sami Bourouis⁶

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 119-131, 2022, DOI:10.32604/iasc.2022.024812 - 15 April 2022

Abstract The detection of the objects in the ariel image has a significant impact on the field of parking space management, traffic management activities and surveillance systems. Traditional vehicle detection algorithms have some limitations as these algorithms are not working with the complex background and with the small size of object in bigger scenes. It is observed that researchers are facing numerous problems in vehicle detection and classification, i.e., complicated background, the vehicle’s modest size, other objects with similar visual appearances are not correctly addressed. A robust algorithm for vehicle detection and classification has been proposed… More >

Ying Tian^{1, *}, Libing Wang¹, Hexin Gu², Lin Fan³

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2397-2412, 2020, DOI:10.32604/cmc.2020.011386 - 16 September 2020

Abstract The application of deep learning in the field of object detection has experienced much progress. However, due to the domain shift problem, applying an off-the-shelf detector to another domain leads to a significant performance drop. A large number of ground truth labels are required when using another domain to train models, demanding a large amount of human and financial resources. In order to avoid excessive resource requirements and performance drop caused by domain shift, this paper proposes a new domain adaptive approach to cross-domain vehicle detection. Our approach improves the cross-domain vehicle detection model from More >

Xiaoliang Wang^{1, *}, Wenhua Song¹, Bowei Zhang¹, Brandon Mausler², Frank Jiang^{1, 3}

CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 135-147, 2019, DOI:10.32604/cmc.2019.05687

Abstract Nowadays, the number of vehicles in China has increased significantly. The increase of the number of vehicles has also led to the increasingly complex traffic situation and the urgent safety measures in need. However, the existing early warning devices such as geomagnetic, ultrasonic and infrared detection have some shortcomings like difficult installation and maintenance. In addition, geomagnetic detection will damage the road surface, while ultrasonic and infrared detection will be greatly affected by the environment. Considering the shortcomings of the existing solutions, this paper puts forward a solution of early warning for vehicle turning meeting More >