International Journal of Advanced Technology and Engineering Exploration ISSN (Print): 2394-5443    ISSN (Online): 2394-7454 Volume-12 Issue-130 September-2025
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  2. 2.8
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Enhancing road crack detection using hybrid PSO-WOA feature selection and random forest classifier

Pallav Kumar1 and Shivangi Mishra2

Assistant Professor, Department of Civil Engineering,MIT Muzaffarpur, 842003,Bihar,India1
Assitant Town Planning Supervisor,Urban Development and Housing Department, Patna, 800015,Bihar,India2
Corresponding Author : Pallav Kumar

Recieved : 05-October-2024; Revised : 10-September-2025; Accepted : 16-September-2025

Abstract

Road crack detection plays a vital role in maintaining infrastructure safety and preventing accidents. A robust approach was presented for road crack detection using a hybrid feature extraction method and a feature selection technique, coupled with a powerful classification algorithm. The hybrid approach combines texture-based features, including multiscale local binary pattern (MSLBP), discrete cosine transform (DCT), speeded-up robust features (SURF), and convolutional neural network (CNN) features, to capture both local and global characteristics of road crack images. To further enhance classification accuracy and reduce dimensionality, a hybrid particle swarm optimization and whale optimization algorithm (PSO-WOA) is employed for feature selection. Hybrid PSO-WOA ensures that the selected features have high relevance to the classification task while minimizing redundancy among them. By leveraging PSO-WOA, the most discriminative features for accurate road crack detection are identified. Subsequently, the selected features are utilized to train a random forest (RF) classifier, a robust and ensemble-based algorithm known for handling complex datasets and maintaining strong generalization capabilities. The RF classifier accurately classifies road crack images into relevant categories, enabling effective detection and localization of cracks on road surfaces. Extensive experiments on a dataset of road crack images were conducted to evaluate the performance of the proposed approach. Various performance metrics, including accuracy, sensitivity, specificity, precision, and F1-Score, were measured to assess the quality of the approach. The results demonstrated that the hybrid feature extraction, PSO-WOA-based feature selection, and RF classification method significantly enhanced road crack detection accuracy and reliability compared to existing techniques.

Keywords

Road crack detection, Hybrid feature extraction, PSO-WOA optimization, Random forest classifier, Image classification, Infrastructure safety.

Cite this article

Kumar P, Mishra S. Enhancing road crack detection using hybrid PSO-WOA feature selection and random forest classifier. International Journal of Advanced Technology and Engineering Exploration. 2025;12(130):1360-1378. DOI : 10.19101/IJATEE.2024.111101818

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