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246 / 2024-03-14 12:29:22
Non-residual unrestricted pruned ultra-faster line detection for edge devices
Edge computing；
Intelligent Equipment Technology
Abstract Accepted
Dongjingdian Liu / China University of Mining and Technology
Line detection with deep learning is a popular visual task that focuses mostly on lane detection. It re


quires quicker inference speed and lower consumption, especially for high-speed edge device applica


tions. Based on the UFAST, we propose the Non-Residual Unrestricted Pruned Ultra-faster (NRUPU) line


detection via a novel model compression method including non-interference structural reconstruction


(NISR), shallow channel priority reservation (SCPR) pruning and non-residual equivalent transformation


(NRET). NISR is a structure reconstruction scheme allocating residual branches into each layer to solve


the cross-layer channel interference in ResNet-18. SCPR pruning directly uses the factors of BN layers to


build channel importance evaluation for backbone and designs channel selection method for head based


on data distribution consistency, reducing the parameters of each layer independently. Then NRET loss


lessly converts the multi-branch model to a single-branch one containing only convolution, linear, and


relu, which reduces implementation complexity on edge devices. These designs follow the theoretical


foundations: the data distribution transformation trend and effect of gradient back-propagation on model


learning ability. Compared with previous pruning methods, our method optimizes not only the parame


ters of the model but also the structure of the model. We train NRUPU in RTX2080Ti and deploy tests on


edge devices NVIDIA Jetson Xavier NX (NJXN) and Atlas 200 DK (A2DK). Extensive experiments are con


ducted on the dataset TuSimple, CULane and our belt dataset with 11,894 data. Results show that NRUPU


achieves over 96% speed increase and over 66% parameter reduction on all datasets within 0.7% accuracy


loss. The FPS can reach 749, 665 and 783 on RTX2080Ti, 133, 117 and 143 on NJNX, 178, 161 and 183 on


A2DK respectively. T
Important Date

  • Conference Date

    May 29

    2024

    to

    Jun 01

    2024

  • May 08 2024

    Draft paper submission deadline

Sponsored By
China University of Mining and Technology
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