Ground-based hail observations with high temporal resolution and precise microphysical quantification remain critically scarce, limiting the validation of radar-based hail detection algorithms and convective-scale numerical models. Existing automatic hail sensors often suffer from small sampling areas, susceptibility to rain interference, and limited automation in post-event processing. We present HailCam, an intelligent hail observation instrument integrating high-definition optical imaging, automated particle collection, and real-time deep learning inference to address critical gaps in time-resolved ground-based hail microphysics measurements. The system employs a ConvNeXt-Tiny architecture with Mask R-CNN for instance segmentation, capturing hailstone number, size distribution, and number flux at one-minute intervals over a 60 cm × 60 cm sampling area. Laboratory validation using synthetic ice spheres (5–45 mm) and polystyrene foam spheres demonstrates 91 % sizing accuracy within ±5 % relative error (RMSE 0.21–1.71 mm) and counting linearity of R² = 0.9989. Field intercomparison with an OTT Parsivel² disdrometer during a nocturnal hail event on 9 May 2025 reveals consistent temporal evolution of hailfall and statistically indistinguishable size distributions (Kolmogorov-Smirnov D = 0.167–0.250, p > 0.84), though absolute counts differ due to distinct phase-discrimination methodologies. HailCam provides co-located, time-stamped measurements essential for validating radar-based hail algorithms and constraining convective-scale numerical models, particularly in complex terrain where remote sensing is challenged.