IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING

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.