Single-pixel imaging (SPI) is a novel technique that captures 2D images using a programmable spatial light modulator (SLM) and a single-pixel detector, instead of conventional 2D array sensors. The image can be reconstructed from the modulation patterns and corresponding 1D bucket measurements. Conventional object detection is performed after a reconstructed image with high fidelity is available. In this paper, an image-free object detection method using the single-pixel measurements is proposed. We designed and trained an end-to-end convolutional neural network to encode and decode a scene for image-free object detection application. The performance of the proposed method is demonstrated using part data from the Voc dataset, which achieves a detection accuracy of 35.41% mAP at a sampling rate below 1.6%.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.