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Optimal deep learning based vehicle detection and classification using chaotic equilibrium optimization algorithm in remote sensing imagery
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AI
DOI：10.1038/s41598-025-02491-0.png)
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Abstract
En 中文
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.
Keywords:
Deep learning
Vehicle detection
Vehicle classification
Chaotic equilibrium optimization algorithm
Remote sensing images
Journal
IF:
3.9
Papers:
27.4W
Citations:
83.5W
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