<p>This paper compares digital image processing (DIP) and deep learning (DL) as a real-time pavement macrotexture detector and estimator of air voids. Normal DIP methods, including Fuzzy Logic Edge Detection, Edge Density Method, Butterworth Band-Pass Filter, and Thresholding Method, were applied to asphalt samples and compared to Mean Texture Depth (MTD). Defect estimation was also done using advanced DL approaches. It was found that Edge Density Method had the greatest predictive accuracy, R² = 0.88, followed by Fuzzy Logic Edge Detection, R² = 0.85, and Butterworth Band-Pass Filter had the lowest predictive accuracy, R² = 0.69, because of sensitivity to aggregate brightness. DL methods performed better in estimating air void, R² = 0.99, which proves that they can be used as an alternative to conventional methods. The analysis of Random Forest verified the reliability of DIP-based methods, and the most feasible ones were DL and thresholding method. Altogether, the developed framework is novel in estimating macrotexture, i.e., a combination of DIP and ML-based strategies that improve the analysis of pavement texture in real-time applications further by increasing its accuracy, efficiency, and scalability to real-world scenarios.</p>