Hybrid Techniques for Object Detection using Deep Learning

Abstract

Deep learning is a one of the major concept of Artificial Intelligence and Machine learning, which deals with the object detection task. On the other hand, a new targeted dataset is built according to commonly used existing datasets, and two networks called Single Shot Multi box Detector (SSD) and You Only Look Once (YOLO) are chosen to work on this new dataset. Through experimentation strengthen the understanding of these networks, and through the analysis of the results, learn the importance of targeted and inclusive datasets for deep learning. In addition, to this optimize the networks for efficient utilization when integrated with the necessary system or application. Further, explore the applications corresponding to these networks. The implementation includes two major concepts. The first concept is Object detection. Object detection is the process of object recognition and classification. There are several Training sets available online for training an object detection model. But the models are not trained to detect the same object from different geographical regions. The second concept is lane detection and steering suggestion. The model detects using the concept of radius or curvature of the road and also distance of the car from both the lane lines. Using these parameters it also gives steering suggestions such as move right or left by a certain distance. In addition to this it gives the distance and speed attributes of the surrounding objects such as cars, motorcycles, etc. Finally, the model developed is capable of detecting all the parameters required in order to be integrated and to create a self-driving car and it can be used efficiently in India. Using the above parameters that are obtained from the model the car can navigate through lanes in real-time. Its improved performance is due to the fact that it can detect road specific objects and because it is specifically trained for Indian roads.