Feature-Selection-Based Attentional-Deconvolution Detector for German Traffic Sign Detection Benchmark

Junho Chung, Sangkyoo Park, Dongsung Pae, Hyunduck Choi, Myotaeg Lim

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

In this study, we propose a novel traffic sign detection algorithm based on the deeplearning approach. The proposed algorithm, which we termed the feature-selection-based attentionaldeconvolution detector (FSADD), is used along with the “you look only once” (YOLO) v5 structure for feature selection. When applying feature selection inside a detection algorithm, the network divides the extracted feature maps after the convolution layer into similar and non similar feature maps. Generally, the feature maps obtained after the convolution layers are the outputs of filters with random weights. Owing to the randomness of the filter, the network obtains various kinds of feature maps with unnecessary components, which degrades the detection performance. However, grouping feature maps with high similarities can increase the relativeness of each feature map, thereby improving the network detection of specific targets from images. Furthermore, the proposed FSADD model has modified sizes of the receptive fields for improved traffic sign detection performance. Many of the available general detection algorithms are unsuitable for the German traffic sign detection benchmark (GTSDB) because of the small sizes of these signs in the images. Experimental comparisons were performed with respect to the GTSDB to show that the proposed FSADD is comparable to the state-of-the-art while detecting 29 kinds of traffic signs with 73.9% accuracy of classification performances.

Original languageEnglish
Article number725
JournalElectronics (Switzerland)
Volume12
Issue number3
DOIs
Publication statusPublished - 2023 Feb

Bibliographical note

Funding Information:
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF) (grant no. NRF-2022R1F1A1073543).

Publisher Copyright:
© 2023 by the authors.

Keywords

  • feature selection
  • German traffic sign detection benchmark
  • traffic sign detection
  • YOLO

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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