2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IP-3.10
Paper Title TRAINING AN EMBEDDED OBJECT DETECTOR FOR INDUSTRIAL SETTINGS WITHOUT REAL IMAGES
Authors Julia Cohen, Carlos Crispim-Junior, Université Lyon 2 - LIRIS (CNRS), France; Jean-Marc Chiappa, DEMS, France; Laure Tougne, Université Lyon 2 - LIRIS (CNRS), France
SessionMLR-APPL-IP-3: Machine learning for image processing 3
LocationArea F
Session Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract In an industrial environment, object detection is a challenging task due to the absence of real images and real-time requirements for the object detector, usually embedded in a mobile device. Using 3D models, it is however possible to create a synthetic dataset to train a neural network, although the performance on real images is limited by the domain gap. In this paper, we study the performance of a Convolutional Neural Network (CNN) designed to detect objects in real-time: Single-Shot Detector (SSD) with a Mobilenet backbone. We train SSD with synthetic images only and apply extensive data augmentation to reduce the domain gap between synthetic and real images. On the T-LESS dataset, SSD performs better than Mask R-CNN trained on the same synthetic images, with MobilenetV2 and MobilenetV3 Large as backbone. Our results also show the huge improvement enabled by an adequate augmentation strategy.