Abstract: An approach is provided for detecting road features. The approach, for example, involves receiving image data associated with one or more road features. The received image data may be associated with ground truth label data corresponding to the one or more road features. The approach further involves determining a set of shadowed regions from one or more images of the received image data. The approach further involves generating artificial shadow data based on the determined set of shadowed regions. The approach further involves augmenting the image data with the generated artificial shadow data by applying the generated artificial shadow data to the received image data. The approach further involves training a machine learning (ML) model based on the augmented image data and the ground truth label data. The ML model is trained to detect the one or more road features.

Abstract: An approach is provided for detecting road features. The approach, for example, involves receiving image data associated with one or more road features. The received image data may be associated with ground truth label data corresponding to the one or more road features. The approach further involves determining a set of shadowed regions from one or more images of the received image data. The approach further involves generating artificial shadow data based on the determined set of shadowed regions. The approach further involves augmenting the image data with the generated artificial shadow data by applying the generated artificial shadow data to the received image data. The approach further involves training a machine learning (ML) model based on the augmented image data and the ground truth label data. The ML model is trained to detect the one or more road features.