Dual Architecture Deep Learning Based Object Detection System for Autonomous Driving
Mahmoud Mahmoud, Ahmed R. Nasser
Abstract
Object detection of autonomous vehicles presents a big challenge for researchers due to the requirements of accuracy and precision in real-time. This work presents a deep learning approach based on a dual architecture design of the network. A highly accurate multi-class network of convolutional neural networks (CNN) is presented for input data classification. A Region-Based Convolutional Neural Networks (Faster R-CNN) network with a modified Feature Pyramid Networks (FPN) is used for better detection of tiny objects and You Only Look Once (YOLOv3) network is used for general detection. Each network independently detects the existence of an object. The decision maps are then fused and compared to decide whether an object is present or not. Faster R-CNN with FPN model reported a higher intersection over Union (IoU) and mean average precision (mAP) than the YOLOv3. This approach is reliable demonstrating an upgrade on the existing state-of-the-art methods of fully connected networks. Index Terms— autonomous driving, computer vision, deep learning, object detection