To Perform Road Signs Recognition for Autonomous Vehicles Using Cascaded Deep Learning Pipeline

Autonomous vehicle is a vehicle that can guide itself without human conduction. It is capable of sensing its environment and moving with little or no human input. This kind of vehicle has become a concrete reality and may pave the way for future systems where computers take over the art of driving. Advanced artificial intelligence control systems interpret sensory information to identify appropriate navigation paths, as well as obstacles and relevant road signs. In this paper, we introduce an intelligent road signs classifier to help autonomous vehicles to recognize and understand road signs. The road signs classifier based on an artificial intelligence technique. In particular, a deep learning model is used, Convolutional Neural Networks (CNN). CNN is a widely used Deep Learning model to solve pattern recognition problems like image classification and object detection. CNN has successfully used to solve computer vision problems because of its methodology in processing images that are similar to the human brain decision making. The evaluation of the proposed pipeline was trained and tested using two different datasets. The proposed CNNs achieved high performance in road sign classification with a validation accuracy of 99.8% and a testing accuracy of 99.6%. The proposed method can be easily implemented for real time application

A Novel Dataset For Intelligent Indoor Object Detection Systems

Indoor Scene understanding and indoor objects detection is a complex high-level task for automated systems applied to natural environments. Indeed, such a task requires huge annotated indoor images to train and test intelligent computer vision applications. One of the challenging questions is to adopt and to enhance technologies to assist indoor navigation for visually impaired people (VIP) and thus improve their daily life quality. This paper presents a new labeled indoor object dataset elaborated with a goal of indoor object detection (useful for indoor localization and navigation tasks). This dataset consists of 8000 indoor images containing 16 different indoor landmark objects and classes. The originality of the annotations comes from two new facts taken into account: (1) the spatial relationships between objects present in the scene and (2) actions possible to apply to those objects (relationships between VIP and an object).This collected dataset presents many specifications and strengths as it presents various data under various lighting conditions and complex image background to ensure more robustness when training and testing objects detectors. The proposed dataset, ready for use, provides 16 vital indoor object classes in order to contribute for indoor assistance navigation for VIP