  The existing segmentation techniques require high-fidelity images as input to
perform semantic segmentation. Since the segmentation results contain most of
edge information that is much less than the acquired images, the throughput gap
leads to both hardware and software waste. In this letter, we report an
image-free single-pixel segmentation technique. The technique combines
structured illumination and single-pixel detection together, to efficiently
samples and multiplexes scene's segmentation information into compressed
one-dimensional measurements. The illumination patterns are optimized together
with the subsequent reconstruction neural network, which directly infers
segmentation maps from the single-pixel measurements. The end-to-end
encoding-and-decoding learning framework enables optimized illumination with
corresponding network, which provides both high acquisition and segmentation
efficiency. Both simulation and experimental results validate that accurate
segmentation can be achieved using two-order-of-magnitude less input data. When
the sampling ratio is 1%, the Dice coefficient reaches above 80% and the pixel
accuracy reaches above 96%. We envision that this image-free segmentation
technique can be widely applied in various resource-limited platforms such as
UAV and unmanned vehicle that require real-time sensing.
