Comparative Analysis of Texture Feature Extraction-Based Machine Learning Algorithms for Road Surface Condition Classification

A Comparison of Supervised Machine Learning Algorithms for Defective Pixel Detection

The proposed framework provides reproducible reference results for evaluating supervised machine learning approaches to defective pixel detection while analyzing the influence of feature extraction window size on classification performance and reconstructed image quality.

Image-based air quality classification: superior performance of a VGG16 feature extraction and random forest pipeline

Air pollution is a critical threat to environmental sustainability and public health, particularly in densely populated countries such as India and Nepal. This paper presents a hybrid pipeline that combines deep learning feature extraction with traditional machine learning classification for image-based air quality assessment. The method employs a two-stage pipeline: feature extraction using a pre-trained VGG16 CNN and classification using SVM and Random Forest (RF) algorithms. The model breaks down air quality into six levels: Good, Moderate, Unhealthy for Sensitive Groups, Unhealthy, Very Unhealthy, and Severe. A publicly available dataset of photographs taken at multiple urban sites in India and Nepal was used to conduct the test. Experimental results show that the Random Forest classifier achieves 97 percent accuracy, compared to 95 percent for the SVM classifier, on the test data. The effectiveness of the method is confirmed by a large-scale evaluation based on precision, recall, F1-score, and ROC-AUC metrics. The findings indicate a significant potential for combining deep feature extraction with traditional machine learning algorithms to create scalable environmental monitoring systems for real-world applications.

A novel road damage detection framework with multi-scale feature enhancement

The health condition of road transportation infrastructure is directly related to driving safety and transportation efficiency. However, existing automated detection technologies still struggle to meet the requirements of large-scale, high-precision, and real-time inspection when confronted with challenges such as significant scale variation of damage targets, strong background texture interference, and fine cracks. This paper proposes a road defect detection model comprising C2f_PBA, CARAFE, and WPIoU, based on the you only look once version 8 (PCW-YOLO). The C2f_PBA module was designed and applied to the backbone network, employing a multi-branch parallel attention mechanism to extract features at different scales. This effectively enhances the model’s ability to capture heterogeneous damage. In the neck network, a CARAFE content-aware upsampling operator is introduced to replace conventional nearest-neighbor interpolation, enabling precise recovery of semantic details and edge information in feature maps via an adaptive kernel prediction mechanism, thereby addressing feature distortion during upsampling. In addition, a variant of wise intersection over union based on minimum point distance intersection over union is proposed WPIoU. The WPIoU loss function combines geometric constraints with a dynamic focusing mechanism to optimize the bounding box regression process, significantly improving the localization accuracy for targets with extreme aspect ratios and low-quality samples. Evaluations using the RDD2022 dataset demonstrate that the PCW-YOLO framework outperforms the traditional YOLOv8 architecture, achieving improvements of 2.1%, 1.1%, and 2.2% in precision, recall, and mAP, respectively. To verify the model’s generalization capabilities, subsequent evaluations were conducted using the SVRDD dataset. The results showed that the optimized network achieved a 5.2% increase in precision, a 1.4% increase in recall, and a 3.8% increase in mAP, thereby providing a more precise and efficient solution for road defect detection.

Hybrid PCA–LBP and Wavelet Scattering Framework for Texture Classification in Color Images

A hybrid framework that integrates Principal Component Analysis, Local Binary Patterns, Wavelet Scattering Transform, and the XGBoost classifier for color texture classification is presented, demonstrating that the two selected complementary feature extraction methods provide a compact yet effective representation for color texture classification on these datasets.

Content-based retrieval of fundus images and diabetic retinopathy detection using variants of local texture features.

Road Condition Recognition Based on Object Classification Using YOLOv8

The quality of road infrastructure is one of the important factors in supporting the safety and comfort of road users as well as the smooth distribution of transportation. Road maintenance requires periodic monitoring by authorized institutions or agencies. Manual road condition monitoring tends to require considerable time, cost, and manpower, and is also prone to subjectivity. Therefore, a computational system capable of performing this task is needed. Based on this background, this study aims to develop a computer vision-based application for recognizing road conditions. Data consisting of road images with proper annotations (damaged or good) were used to train the YOLOv8 vision model. Our test found that the system accuracy, precision, recall, and F1-score is 0.96, 0.93, 1.00, and 0.96 respectively. The developed application allows users to input road images through a live camera and obtain real-time road condition classification results.