{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T15:03:14Z","timestamp":1779202994433,"version":"3.51.4"},"reference-count":38,"publisher":"World Scientific Pub Co Pte Ltd","issue":"14","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"<jats:p> Road damage detection utilizing remote sensing images is a crucial component of intelligent transportation systems (ITS), which support the enhancement of road safety and traffic efficiency. ITS employs new technologies to optimize and manage transportation networks, including airports, highways and railways. Within ITS, road damage detection using remote sensing images identifies and maps damages such as potholes, cracks and other structural defects. This technique is vital for improving road safety, enhancing maintenance efficiency and reducing repair costs. Recently, deep learning (DL) algorithms, particularly convolutional neural networks (CNNs), have demonstrated promising results in road damage detection using RSIs. These techniques automatically identify damages during the feature extraction process, eliminating the need for manual feature engineering. This study introduces the Tuna Swarm Optimization with deep learning-driven road damage detection for intelligent transportation systems (TSODL-RDDITS) method using remote sensing images. The proposed TSODL-RDDITS aims to categorize different types of road damage in RSIs. To achieve this, TSODL-RDDITS employs the YOLO-v5 object detector for efficient road damage detection. Additionally, the YOLO-v5 model uses CSPDarknet53 as its backbone network, coupled with a symbiotic organism search (SOS)-based hyperparameter optimizer. For road damage classification, TSODL-RDDITS utilizes an attention-based long short-term memory (ALSTM) model and its hyperparameters can be adjusted by the use of TSO algorithm. A wide range of simulation analyses was performed to exhibit the superior performance of the TSODL-RDDITS method. The experimental outcomes stated the enhanced outcomes of the TSODL-RDDITS algorithm over other existing approaches. <\/jats:p>","DOI":"10.1142\/s0218126625501348","type":"journal-article","created":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T11:49:47Z","timestamp":1730288987000},"source":"Crossref","is-referenced-by-count":2,"title":["Road Damage Detection for Intelligent Transportation Systems Using Remote Sensing Images and Deep Learning Optimization"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-2103-5099","authenticated-orcid":false,"given":"Kejun","family":"Li","sequence":"first","affiliation":[{"name":"Electrical and Electronic Engineering College, Hubei University of Technology, Wuhan 430068, P. R. 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