An Efficient Semantic Segmentation Decoder for In-Car or Distributed Applications (2510.16747v1)

Abstract: Modern automotive systems leverage deep neural networks (DNNs) for semantic segmentation and operate in two key application areas: (1) In-car, where the DNN solely operates in the vehicle without strict constraints on the data rate. (2) Distributed, where one DNN part operates in the vehicle and the other part typically on a large-scale cloud platform with a particular constraint on transmission bitrate efficiency. Typically, both applications share an image and source encoder, while each uses distinct (joint) source and task decoders. Prior work utilized convolutional neural networks for joint source and task decoding but did not investigate transformer-based alternatives such as SegDeformer, which offer superior performance at the cost of higher computational complexity. In this work, we propose joint feature and task decoding for SegDeformer, thereby enabling lower computational complexity in both in-car and distributed applications, despite SegDeformer's computational demands. This improves scalability in the cloud while reducing in-car computational complexity. For the in-car application, we increased the frames per second (fps) by up to a factor of $11.7$ ($1.4$ fps to $16.5$ fps) on Cityscapes and by up to a factor of $3.5$ ($43.3$ fps to $154.3$ fps) on ADE20K, while being on-par w.r.t.\ the mean intersection over union (mIoU) of the transformer-based baseline that doesn't compress by a source codec. For the distributed application, we achieve state-of-the-art (SOTA) over a wide range of bitrates on the mIoU metric, while using only $0.14$\% ($0.04$\%) of cloud DNN parameters used in previous SOTA, reported on ADE20K (Cityscapes).