This study empirically evaluates deep learning–based image analysis systems using real-world UAV imagery and investigates their applicability to public safety surveillance and enforcement. Field experiments were conducted with a police-operated mission drone and a high-resolution commercial UAV platform, covering daytime electro-optical (EO) and nighttime infrared (IR) imaging across varying altitudes and observation distances. Detection performance was assessed via valid detection ratio, false positive rate, and effective detection range. Results show that detection reliability is strongly constrained by input image resolution, target spatial scale, and environmental conditions, even when identical deep learning algorithms are applied. The mission drone maintained useful performance at short range and low altitude but degraded rapidly at medium and long ranges, whereas the high-resolution platform sustained stable detection over extended distances. Using ground sampling distance (GSD)-based optical equivalence, UAV imaging parameters were mapped to equivalent ground-based configurations, yielding conceptually derived performance thresholds linking image resolution and observation distance to detection reliability. These thresholds are interpreted in relation to ground-based intelligent traffic enforcement requirements, though empirical validation under fixed-camera conditions remains for future research. These findings highlight that minimum system-level imaging requirements, rather than algorithmic accuracy alone, are essential for designing, procuring, and certifying public-sector video analytics systems.