Alotaibi Youseef, Nagappan Krishnaraj, Thanarajan Tamilvizhi, Rajendran Surendran
Department of Software Engineering, College of Computing, Umm Al-Qura University, Makkah, 21955, Saudi Arabia.
Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India.
Sci Rep. 2025 May 23;15(1):17921. doi: 10.1038/s41598-025-02491-0.
Remote sensing images (RSI), such as aerial or satellite images, produce a large-scale view of the Earth's surface, which gets them used to track and monitor vehicles from several settings, like border control, disaster response, and urban traffic surveillance. Vehicle detection and classification using RSIs is a vital application of computer vision and image processing. It contains locating and identifying vehicles from the image. It is done using many approaches that have object detection approaches, namely YOLO, Faster R-CNN, or SSD, which utilize deep learning (DL) to locate and identify the image. Additionally, the classification of vehicles from RSIs contains classification of them based on their variety, such as trucks, motorcycles, cars or buses, utilizing machine learning (ML) techniques. This article designed and developed an automated vehicle type detection and classification using a chaotic equilibrium optimization algorithm with deep learning (VDTC-CEOADL) on high-resolution RSIs. The VDTC-CEOADL technique presented examines high-quality RSIs for the accurate detection and classification of vehicles. The VDTC-CEOADL technique employs a YOLO-HR object detector with a residual network as the backbone model to accomplish this. In addition, CEOA based hyperparameter optimizer is designed for the parameter tuning of the ResNet model. For the vehicle classification process, the VDTC-CEOADL technique exploits the attention-based long-short-term memory (ALSTM) mod-el. Performance validation of the VDTC-CEOADL technique is validated on a high-resolution RSI dataset, and the results portrayed the supremacy of the VDTC-CEOADL technique in terms of different measures.
遥感图像（RSI），如航空或卫星图像，呈现出地球表面的大规模视图，这使得它们被用于在多种场景下跟踪和监测车辆，如边境管制、灾害响应和城市交通监控。使用RSI进行车辆检测和分类是计算机视觉和图像处理的一项重要应用。它包括从图像中定位和识别车辆。这是通过许多具有目标检测方法的途径来完成的，即YOLO、Faster R-CNN或SSD，它们利用深度学习（DL）来定位和识别图像。此外，从RSI中对车辆进行分类包括根据车辆的种类，如卡车、摩托车、汽车或公共汽车，利用机器学习（ML）技术进行分类。本文设计并开发了一种基于深度学习的混沌平衡优化算法（VDTC-CEOADL）的自动车辆类型检测和分类方法，用于高分辨率RSI。所提出的VDTC-CEOADL技术检查高质量的RSI，以实现车辆的准确检测和分类。VDTC-CEOADL技术采用以残差网络为骨干模型的YOLO-HR目标检测器来实现这一目标。此外，基于CEOA的超参数优化器被设计用于ResNet模型的参数调整。对于车辆分类过程，VDTC-CEOADL技术利用基于注意力的长短时记忆（ALSTM）模型。在高分辨率RSI数据集上对VDTC-CEOADL技术进行了性能验证，结果表明VDTC-CEOADL技术在不同指标方面具有优势。
