The global deterioration of transportation infrastructure necessitates automated, high-precision pavement monitoring systems. While Unmanned Aerial Vehicles (UAVs) provide unparalleled spatial flexibility for infrastructure inspection, aerial photogrammetry suffers from severe environmental degradations, including motion blur, specular glare, and deep structural shadows. This research investigates the quantitative impact of spatial and photogrammetric preprocessing techniques on the detection accuracy of deep learning architectures. Evaluations conducted on global benchmarks, notably the RDD2022 and UAV-PDD2023 datasets, reveal that algorithmic input optimization directly dictates neural network performance. Specifically, applying Contrast Limited Adaptive Histogram Equalization (CLAHE) enhanced fine crack detection accuracy from 88% to 95% by improving local feature visibility without amplifying global noise. High-frequency amplification via Unsharp Masking drove a profound 12.77% improvement in Mean Average Precision (mAP) for Faster R-CNN models, effectively preventing the loss of hairline features during down-sampling. Furthermore, illumination-invariant Retinex transformations increased information entropy by 63% in low-light environments, enabling a 97.5% overall recognition accuracy. Finally, the shift toward implicit preprocessing mechanisms in single-stage YOLO variants, achieved through integrated attention modules, reduced computational parameters by 88% while maintaining a robust mAP@0.5 of 63.2%. This study proves that the systematic integration of mathematical image filters with convolutional feature extraction is a fundamental requirement for achieving reliable, autonomous aerial infrastructure assessment. By bridging the gap between raw optical data and high-fidelity feature extraction, this research establishes a robust framework for real-time, weather-invariant roadway condition assessment in dynamic operational environments.
