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

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 positives likely to happen in… 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

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 to overcome the limitation of… 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

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 image space and feature space.… 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 based on image acquisition and… More >