Leveraging high-resolution remote sensing images for vehicle type detection using sparrow search optimization with deep learning

• Published in: Multimedia tools and applications Vol. 83; no. 32; pp. 77549 - 77564
• Main Authors: Umamaheswari, Ramisetti, Avanija, J.
• Format: Journal Article
• Language: English
• Published: New York Springer US 26.02.2024; Springer Nature B.V
• Subjects: Agricultural aircraft; Algorithms; Artificial neural networks; Classification; Computer Communication Networks; Computer Science; Computer vision; Data Structures and Information Theory; Datasets; Deep learning; Earth surface; Environmental monitoring; High resolution; Image processing; Image quality; Image resolution; Machine learning; Multimedia Information Systems; Object recognition; Optimization; Remote sensing; Satellite imagery; Spacecraft recovery; Special Purpose and Application-Based Systems; Urban areas; Urban planning; Deep learning; Computer vision; Remote sensing images; Vehicle classification; Sparrow search optimizer
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-024-18273-1

High-resolution remote sensing images (RSI) refer to images captured from a distance, usually from an aircraft or satellite that provide details about the Earth's surface. It can be used in several application areas like environmental monitoring, urban planning, agriculture, and disaster response. In urban planning, high-resolution imagery is used to monitor the growth of urban areas or to recognize the area that requires infrastructure improvement. Vehicle detection is a significant way of understanding high-resolution RSIs. Vehicle detection and classification on high-resolution RSI is a difficult task that needs a group of computer vision (CV), image processing, and machine learning (ML) algorithms. Deep convolutional neural network (DCNN) based techniques have attained recent outcomes in many object detection datasets and have enriched several CV tasks. This article designed and developed a sparrow search optimization algorithm with deep learning for vehicle type detection and classification (SSOADL-VTDC) technique on high-resolution remote sensing images. The presented SSOADL-VTDC technique examines the high-quality RSIs for the accurate detection and classification of vehicles. To accomplish this, the SSOADL-VTDC technique employs a YOLOv5 object detector with a Residual Network as a backbone approach. In addition, the SSOADL-VTDC technique uses SSOA based hyperparameter optimizer designed for the parameter tuning of the YOLOv5 model. For the vehicle classification process, the SSOADL-VTDC technique exploits the softmax classifier. The simulation validation of the SSOADL-VTDC approach was validated on a high-resolution RSI dataset and the outcomes demonstrated the greater of the SSOADL-VTDC methodology in terms of different measures.