Convolutional neural networks-based object detection algorithm by jointing semantic segmentation for images

Baohua QIANG, Ruidong CHEN, Mingliang ZHOU*, Yuanchao PANG, Yijie ZHAI, Minghao YANG

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

13 Citations (Scopus)

Abstract

In recent years, increasing image data comes from various sensors, and object detection plays a vital role in image understanding. For object detection in complex scenes, more detailed information in the image should be obtained to improve the accuracy of detection task. In this paper, we propose an object detection algorithm by jointing semantic segmentation (SSOD) for images. First, we construct a feature extraction network that integrates the hourglass structure network with the attention mechanism layer to extract and fuse multi-scale features to generate high-level features with rich semantic information. Second, the semantic segmentation task is used as an auxiliary task to allow the algorithm to perform multi-task learning. Finally, multi-scale features are used to predict the location and category of the object. The experimental results show that our algorithm substantially enhances object detection performance and consistently outperforms other three comparison algorithms, and the detection speed can reach real-time, which can be used for real-time detection.

Original languageEnglish
Article number5080
Pages (from-to)1-14
Number of pages14
JournalSensors (Switzerland)
Volume20
Issue number18
DOIs
Publication statusPublished - 2 Sept 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Attention mechanism
  • Hourglass network
  • Object detection
  • Semantic segmentation
  • Sensor

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