Optimal deep learning based vehicle detection and classification using chaotic equilibrium optimization algorithm in remote sensing imagery
Abstract 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 classif...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-02491-0 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849731811514515456 |
|---|---|
| author | Youseef Alotaibi Krishnaraj Nagappan Tamilvizhi Thanarajan Surendran Rajendran |
| author_facet | Youseef Alotaibi Krishnaraj Nagappan Tamilvizhi Thanarajan Surendran Rajendran |
| author_sort | Youseef Alotaibi |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-a62ae58b6ea94ea1a4052c202193aab7 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a62ae58b6ea94ea1a4052c202193aab72025-08-20T03:08:25ZengNature PortfolioScientific Reports2045-23222025-05-0115111610.1038/s41598-025-02491-0Optimal deep learning based vehicle detection and classification using chaotic equilibrium optimization algorithm in remote sensing imageryYouseef Alotaibi0Krishnaraj Nagappan1Tamilvizhi Thanarajan2Surendran Rajendran3Department of Software Engineering, College of Computing, Umm Al-Qura UniversityDepartment of Networking and Communications, School of Computing, SRM Institute of Science and TechnologyDepartment of Computer Science and Engineering, Panimalar Engineering CollegeDepartment of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical SciencesAbstract 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.https://doi.org/10.1038/s41598-025-02491-0Deep learningVehicle detectionVehicle classificationChaotic equilibrium optimization algorithmRemote sensing images |
| spellingShingle | Youseef Alotaibi Krishnaraj Nagappan Tamilvizhi Thanarajan Surendran Rajendran Optimal deep learning based vehicle detection and classification using chaotic equilibrium optimization algorithm in remote sensing imagery Scientific Reports Deep learning Vehicle detection Vehicle classification Chaotic equilibrium optimization algorithm Remote sensing images |
| title | Optimal deep learning based vehicle detection and classification using chaotic equilibrium optimization algorithm in remote sensing imagery |
| title_full | Optimal deep learning based vehicle detection and classification using chaotic equilibrium optimization algorithm in remote sensing imagery |
| title_fullStr | Optimal deep learning based vehicle detection and classification using chaotic equilibrium optimization algorithm in remote sensing imagery |
| title_full_unstemmed | Optimal deep learning based vehicle detection and classification using chaotic equilibrium optimization algorithm in remote sensing imagery |
| title_short | Optimal deep learning based vehicle detection and classification using chaotic equilibrium optimization algorithm in remote sensing imagery |
| title_sort | optimal deep learning based vehicle detection and classification using chaotic equilibrium optimization algorithm in remote sensing imagery |
| topic | Deep learning Vehicle detection Vehicle classification Chaotic equilibrium optimization algorithm Remote sensing images |
| url | https://doi.org/10.1038/s41598-025-02491-0 |
| work_keys_str_mv | AT youseefalotaibi optimaldeeplearningbasedvehicledetectionandclassificationusingchaoticequilibriumoptimizationalgorithminremotesensingimagery AT krishnarajnagappan optimaldeeplearningbasedvehicledetectionandclassificationusingchaoticequilibriumoptimizationalgorithminremotesensingimagery AT tamilvizhithanarajan optimaldeeplearningbasedvehicledetectionandclassificationusingchaoticequilibriumoptimizationalgorithminremotesensingimagery AT surendranrajendran optimaldeeplearningbasedvehicledetectionandclassificationusingchaoticequilibriumoptimizationalgorithminremotesensingimagery |