Visual Detection of Road Cracks for Autonomous Vehicles Based on Deep Learning

Abstract

Detecting road cracks is essential for inspecting and assessing the integrity of concrete pavement structures. Traditional image-based methods often require complex preprocessing to extract crack features, making them challenging when dealing with noisy concrete surfaces in diverse real-world scenarios, such as autonomous vehicle road detection. This study introduces an image-based crack detection approach that combines a Random Forest machine learning classifier with a deep convolutional neural network (CNN) to address these challenges. Three state-of-the-art models, namely MobileNet, InceptionV3, and Xception, were employed and trained using a dataset of 30,000 images to build an effective CNN. A systematic comparison of validation accuracy across various base learning rates identified a base learning rate of 0.001 as optimal, achieving a maximum validation accuracy of 99.97%. This optimal learning rate was then applied in the subsequent testing phase. The robustness and flexibility of the trained models were evaluated using 6,000 test photos, each with a resolution of 224 × 224 pixels, which were not part of the training or validation sets. The outstanding results, boasting a remarkable 99.95% accuracy, 99.95% precision, 99.94% recall, and a matching 99.94% F1 Score, unequivocally affirm the efficacy of the proposed technique in precisely identifying road fractures in photographs taken on real concrete surfaces.

Performance Evaluation of Convolutional Neural Network Models for Classification of Highway Hidden Distresses with GPR B-Scan Images

“…Experimental results show that SqueezeNet has higher sensitivity in the validation phase. Meftah et al combined a random forest machine learning classifier with MobileNet, InceptionV3, and Xception to construct an efficient CNN model, confirming the model's effectiveness in identifying road cracks on real concrete surfaces [34]. In the detection task, Ma et al used VGG16 as the basic convolutional network to propose a road crack detection method based on a multi-feature layer convolutional neural network, thereby improving the accuracy of road crack identification [35].…”

Deep learning–based eye tracking system to detect distracted driving

“…Ci et al [28] enhanced detection rates by incorporating semantic segmentation and recognition algorithms into a Bayesian framework. Meftah et al [29] achieved an impressive 99.95% accuracy rate using a cuttingedge deep learning model, while Choi et al [30] obtained superior results with rural datasets. Jiang et al [31] suggested an XGBoost-based pedestrian detection method, optimizing the XGBoost model with a genetic algorithm.…”