Automatic crack detection in civil infrastructure based on a hybrid fine tuned MnasNet and adaptive patching

Detection of cracks is a critical area of automation that would help guarantee the safety and durability of the civil infrastructure at the lowest cost of upkeep. Current image processing and deep learning models are usually weak in real-life scenarios, require excessive resources, or feature manual features extraction. To overcome these shortcomings, this paper presents a new vision-based framework of crack detection that combines the lightweight MnasNet CNN with the Upgraded Lotus Effect Optimization (ULEO) algorithm that automatically adjusts the hyperparameters. The first structural health monitoring use of chaos theory and Levy flight dynamics is ULEO, which is designed to improve convergence and eliminate local optima. Another dynamic patching mechanism used in the framework is that the patch sizes utilize the complexity of the image to adjust dynamically to enhance accuracy without causing a run time increase. The proposed method was tested on the CRACK500 dataset and with various cross-validation configurations, it achieved an accuracy of 97.1, sensitivity of 96.0, specificity of 97.8, and an F1-score of 95.6, outperforming other benchmarks including VGG16, OpenMV-CNN, XGBoost and XAI/t-SNE. Particularly, the proposed model provides this performance during a 0.05 s per image computational time, which makes it more efficient and suitable to use in mobile and embedded platforms. The high accuracy and low computational intensity of the MnasNet/ULEO model make it especially appropriate with real-time and mobile-based crack inspection, which creates a new self-optimized intelligent vision model of defect detection in civil, mechanical, and industrial systems.