Traffic Sign Detection and Recognition (TSDR) is a crucial part of Autonomous Vehicles (AVs) for safe and efficient navigation. Recent advancements in Deep Learning (DL) and 5G technology have significantly improved the perception of AV by enabling real-time data processing. However, real-world challenges such as occlusions from vehicles, roadside objects, and unfavorable traffic conditions affect TSDR performance. Detection models like Faster Region-based Convolutional Neural Network (Faster R-CNN) enhanced TSDR accuracy, but the occlusions remain a significant challenge. This paper proposes a novel feature-based Generative Adversarial Network (GAN) to regenerate occluded traffic signs to improve detection accuracy. The GAN-generated features are fused with the Faster R-CNN-generated features. The architecture of GAN is tiny and integrated at the feature level within Faster R-CNN to minimize computational overhead. Thus, it leverages real-time processing and effective model deployment for AV. An artificial occlusion insertion algorithm has been proposed to train and evaluate the proposed approach against the occluded traffic sign. The experimental results demonstrate that the proposed approach improves the accuracy by 4.2% on level one occlusion, 12% on level two occlusion, and 6.5% on the augmented (occluded and clean) data. Therefore, the proposed approach is more adaptable for 5G-powered intelligent transportation systems.

Biometric features are extracted from a complex pattern and stored as high dimensional data. These data do not follow traditional sorting order like numerical and alphabetical data. Hence, a linear search method makes the identification process extremely slow as well as increases the false acceptance rate beyond an acceptable range. To address this problem, we propose an efficient indexing mechanism to retrieve iris biometric templates using Gabor energy features. The Gabor energy features are calculated from the preprocessed iris texture in different scales and orientations to generate a 12-dimensional index key for an iris template. An index space is created based on the values of index keys of all individuals. A candidate set is retrieved from the index space based on the values of query index key. Next, we rank the retrieved candidates according to their occurrences. If the identity of the query template is matched, then it is a hit, otherwise a miss. We have experimented our approach with Bath, CASIA-V3-Interval, CASIA-V4-Thousand, MMU2, and WVU iris databases. Our proposed approach gives 11.3%, 14.5%, 16.3%, 13.5%, and 10.3% penetration rates and 98.2%, 91.1%, 90.7%, 85.2%, and 96% hit rates for Bath, CASIA-V3-Interval, CASIA-V4-Thousand, MMU2, and WVU iris database, respectively, when we consider the retrieving templates up to the fifth rank. Experiments substantiate that our approach is capable of retrieving biometric data with a higher hit rate and lower penetration rate compared to the existing approaches. Application of Gabor energy features to index iris data proves to be effective for fast and accurate retrieval. With our proposed approach, it is possible to retrieve a set of iris templates similar to the query template in the order of milliseconds and is independent of sizes of databases.