LARNet:Towards Lightweight, Accurate and Real-time Salient Object Detection

Zhenyu Wang, Yunzhou Zhang, Yan Liu, Cao Qin, Sonya A. Coleman, Dermot Kerr

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Salient object detection (SOD) has rapidly developed in recent years, and detection performance has greatly improved. However, the price of these improvements is increasingly complex networks that require more computing resources and sacrifice real-time performance. This makes it difficult to deploy these approaches on devices with limited computing resources (such as mobile phones, embedded platforms, etc.). Considering recently developed lightweight SOD models, their detection and real-time performance are always compromised in demanding practical application scenarios. To solve these problems, we propose a novel lightweight SOD method called LARNet and its corresponding extremely lightweight method LARNet* according to application requirements. These methods balance the relationship between lightweight requirements, detection accuracy and real-time performance. First, we propose a saliency backbone network tailored for SOD, which removes the need for pre-training with ImageNet and effectively reduces feature redundancy. Subsequently, we propose a novel context gating module (CGM), which simulates the physiological mechanism of human brain neurons and visual information processing, and realizes the deep fusion of multilevel features at the global level. Finally, the saliency map is output after fusion of multi-level features. Extensive experiments on popular benchmark datasets demonstrate that the proposed LARNet (LARNet*) achieves 98 (113) FPS on a GPU and 3 (6) FPS on a CPU. With approximately 680K (90K) parameters, the model has significant performance advantages over (extremely) lightweight methods, even surpassing some heavyweight models
Original languageEnglish
Pages (from-to)5207-5222
Number of pages16
JournalIEEE Transactions on Multimedia
Early online date3 Nov 2023
Publication statusPublished (in print/issue) - 21 Mar 2024

Bibliographical note

Publisher Copyright:
© 2023 IEEE.


  • Context gating module
  • feature fusion
  • lightweight
  • saliency backbone network
  • salient object detection
  • Object detection
  • Real-time systems
  • Neurons
  • Feature extraction
  • visualization
  • Computational modeling


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