An occlusion-resilient mmWave imaging radar-based object recognition system for advanced driver-assistance systems (ADAS) of construction machinery application is developed. As ADAS for construction sites, millimeter wave application is required in poor visibility environments such as nighttime, bad weather, and muddy conditions where object recognition by RGB cameras and LiDAR is difficult. A remaining technical challenge for ADAS is occlusion. Two techniques are proposed to improve the accuracy in occlusion scenes. First is a technique which generates simulated training data for occlusion environment to improve accuracy while reducing the cost for the training data preparation. The second is a parallel inference DNN architecture which enables object recognition with high accuracy in both normal and occlusion scenes by running two DNNs optimized respectively for normal and occlusion scenes in parallel. The object recognition accuracy of mAP50 in occlusion scenes improves by 15 points compared to the conventional technique. The decrease in recognition accuracy in non-occlusion scenes is only 4 points.