Instance Segmentation Evaluation for Traffic Signs

Siow, Shi Heng and Shamsudin, Abu Ubaidah and Soomro, Zubair Adil and Ahmad, Anita and Abdul Rahim, Ruzairi and Ahmad, Mohd Khairul Ikhwan and Adriansyah, Andi (2024) Instance Segmentation Evaluation for Traffic Signs. Journal of Advanced Research in Applied Sciences and Engineering Technology, 34 (2). pp. 327-341. ISSN 2462-1943

Text J17153_357848f5090ea08a5f811eabd05736d0.pdf Restricted to Registered users only Download (719kB) | Request a copy

Abstract

This research paper focuses on developing a traffic sign recognition system based on the You Only Look At Coefficients (YOLACT) model, a one-stage instance segmentation model that offers high performance in terms of accuracy and reliability. However, the YOLACT performance was influenced by various conditions such as day/night and different angles of objects. Therefore, this study aims to evaluate the impact of different angles and environments on the system performance. The paper discusses the framework, backbone structure, prototype generation branch, mask coefficient, and mask assembly used in the system. ResNet-101 and ResNet-50 were used as the backbone structure to extract feature maps of objects in the input image. The prototype generation branch generates prototype masks using fully convolutional networks (FCN), and the mask coefficient branch generates the Mask assembly using the sigmoid nonlinearity. Two models, YOLACT and Mask-RCNN, were evaluated by mean precision (mAP) and frames per second (FPS) with the front view dataset. The results show that YOLACT outperforms Mask-RCNN in terms of accuracy and speed. For an image resolution of 550x550, YOLACT with Resnet-101 is considered the best model in this article since it achieves over 80% precision, recall, specificity, and accuracy in various conditions such as day, night, left and right, and forward-looking angles.

Item Type:	Article
Uncontrolled Keywords:	Instance Segmentation; Traffic Sign Recognition; YOLACT; Deep Learning Approach; Image Segmentation
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Electrical and Electronic Engineering > FKEE
Depositing User:	Mr. Mohamad Zulkhibri Rahmad
Date Deposited:	27 Nov 2024 07:31
Last Modified:	27 Nov 2024 07:33
URI:	http://eprints.uthm.edu.my/id/eprint/11728

Actions (login required)

View Item