Recognizing the Indian Cautionary Traffic Signs usingGAN, Improved MaskR‐CNN, and Grab Cut

Abstract

The detection and classification of traffic signs is a major challenge for self-driving vehicles. The task can be narrowed down to detecting and classifying Indian Cautionary Traffic Signs (ICTS). In this proposed work, the difficulty of detecting and identifying Indian Cautionary Traffic Signs is addressed, and sincere attempts have been made to attain a possible solution using a Generative Adversarial Network (GAN), Improved Mask R-CNN, and GrabCut algorithms. Data augmentation is done using Cascade Pyramid Generative Adversarial Network (CP-GAN) to upsize the data set. The Mask R-CNN with certain adoptions termed Improved mask RCNN is used in conjunction with the Grab-Cut method to handle ICTS detection and identification through automatic end-to-end learning. Initially, Improved Mask R-CNN generates a pixel-by-pixel segmentation mask for each item in the input sample. Masks developed using Improved Mask R-CNN are not always clean, that is, some background pixels are often seen in the foreground segmentation. Hence, the generated masks are refined using the Grab Cut algorithm to enhance image segmentation. This combined approach works well in isolating the traffic signs from the ground truth images. Improved Mask R-CNN attained better performance in the overall performance of traffic sign detection in the Indian data-set. This method recognizes 40 different types of cautionary traffic signs from the unique Indian data set. The results are provided for complicated traffic sign categories that have not been addressed before. The proposed technique is trained and evaluated on ICTS, GTSDB, STSD, LISA, and DITS data sets, and it is also compared against cutting-edge object recognition methods like Mask RCNN, Faster RCNN, SSD, and YOLOv3.

Mask R-CNN Approach to Real-Time Lane Detection for Autonomous Vehicles

“…Convolutional Neural Networks (CNNs) have demonstrated great success in various computer vision tasks, including lane detection [15]. These networks learn hierarchical features from raw images, enabling them to automatically learn and adapt to different scenarios.…”