Development of an Ensembled Meta-Deep Learning Model for Semantic Road-Scene Segmentation in an Unstructured Environment

Abstract

Road scene segmentation is an integral part of the Intelligent Transport System (ITS) for precise interpretation of the environment and safer vehicle navigation. Traditional segmentation methods have faced difficulties in meeting the requirements of unstructured and complex image segmentation. Therefore, the Deep-Neural Network (DNN) plays a significant role in effectively segmenting images with multiple classes in an unstructured environment. In this work, semantic segmentation models such as U-net, LinkNet, FPN, and PSPNet are updated to use classification networks such as VGG19, Resnet50, Efficientb7, MobilenetV2, and Inception V3 as pre-trained backbone architectures, and the performance of each updated model is compared with the unstructured Indian Driving-Lite (IDD-Lite) dataset. In order to improve segmentation performance, a stacking ensemble approach is proposed to combine the predictions of a semantic segmentation model across different backbone architectures using a simple grid search method. Thus, four ensemble models are formed and analyzed on the IDD-Lite dataset. The two metrics Intersection over Union (IoU or Jaccard index) and Dice coefficient (F1 score) are used to assess the segmentation performance of each ensemble model. The results show that an ensemble of U-net with different backbone architectures is more efficient than other ensemble models. This model has achieved 73.12% and 76.67%, respectively, in IoU and F1 scores.

Keywords

• semantic segmentation
• ensembling algorithms
• computer vision
• IDD
• segmentation models
• road scene perception