A novel deep neural network for hidden target detection in images


Hendaoui R., NABIYEV V.

Kuwait Journal of Science, cilt.49, sa.4, ss.1-9, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 49 Sayı: 4
  • Basım Tarihi: 2022
  • Doi Numarası: 10.48129/kjs.15249
  • Dergi Adı: Kuwait Journal of Science
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Arab World Research Source, zbMATH
  • Sayfa Sayıları: ss.1-9
  • Anahtar Kelimeler: Concealed people, hidden target, neural network, target identification, target segmentation
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

The significant similarity between the hidden target and the background makes it difficult to find camouflaged people, such as warriors in warfare, or even camouflaged objects in natural environments. Hence, it is hard to ascertain these concealed targets. To address this issue, a novel deep neural network is proposed in this paper that produces an estimated mask within the hidden target for an input image. Our approach consists of two phases: hidden target segmentation and hidden target identification. For the first phase, we propose the Multilevel Attention Network (MA-Net), which generates the camouflaged target mask based on a Multi-Attention Module (MAM) that helps distinguish the hidden people from the background. Later on, the concealed target will be highlighted in the second phase. Experimental results on the camouflaged people dataset demonstrate that our proposed method can achieve state-of-the-art performance for hidden target detection.