ECDNet: Efficient siamese convolutional network for real-time small object change detection from ground vehicles

Sander Klomp, Dennis van de Wouw, Peter de With

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademic

2 Citaten (Scopus)
152 Downloads (Pure)

Samenvatting

Change detection from ground vehicles has various applications, such as the detection of roadside Improvised Explosive Devices (IEDs). Although IEDs are hidden, they are often accompanied by visible markers, which can be any kind of object. Because of this, any suspicious change in the environment compared to an earlier moment in time, should be detected. Little work has been published to solve this ill-posed problem using deep learning. This paper shows the feasibility of applying convolutional neural networks (CNNs) to HD video, to accurately predict the presence and location of such markers in real time. The network is trained for the detection of pixel-level changes in HD video, compared to an earlier reference recording. We investigate Siamese CNNs in combination with an encoder-decoder architecture and introduce a modified double-margin contrastive loss function, to achieve pixel-level change detection results. Our dataset consists of seven pairs of challenging real-world recordings with geo-tagged test objects. The proposed network architecture is capable of comparing two images of 1920×1440 pixels in 150 ms on a GTX1080Ti GPU. The proposed network significantly outperforms state-of-the-art networks and algorithms on our dataset in terms of F-1 score, on average by 0.28.
Originele taal-2Engels
TitelElectronic Imaging
SubtitelIntelligent Robotics and Industrial Applications using Computer Vision 2019
UitgeverijIS&T
Aantal pagina's7
DOI's
StatusGepubliceerd - 13 jan. 2019
EvenementElectronic Imaging: Intelligent Robotics and Industrial Applications using Computer Vision 2019 - Hyatt Regency San Francisco Airport, Burlingame, Verenigde Staten van Amerika
Duur: 13 jan. 201917 jan. 2019

Congres

CongresElectronic Imaging
Land/RegioVerenigde Staten van Amerika
StadBurlingame
Periode13/01/1917/01/19

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