Asphalt pavements are vulnerable to damage from natural disasters, accidents, and human activities, often resulting in surface cracks. Such failures increase maintenance costs, vehicle operation expenses, traffic delays, and safety hazards. Early and accurate detection of these defects is essential for enhancing infrastructure longevity and ensuring road user safety. Manual monitoring is time-consuming and subjective; thus, automated pavement failure detection has become critical. Recent advances in computer vision have enabled improved identification of pavement defects. This study compares two automated approaches: an image recognition model using Convolutional Neural Networks (CNN) and a pattern recognition model using the LANCZOS algorithm. The CNN model processes pavement images to extract key visual features for crack detection, while the LANCZOS-based model applies a machine learning approach to identify failure patterns within structured datasets. Both models were evaluated using standard performance metrics, including accuracy, precision, recall, and F1 score. Results indicate that the pattern recognition model outperformed the image recognition model, achieving 90% accuracy compared to 60%. The findings highlight the efficiency of pattern-based analysis in pavement failure detection and offer valuable insights into the comparative performance of model architectures for future deployment in automated infrastructure monitoring systems.