Parallel Algorithms for Computing the Tensor-Train Decomposition

Shi, Tianyi; Ruth, Maximilian; Townsend, Alex

doi:10.1137/21m146079x

Cited by 5 publications

(1 citation statement)

References 35 publications

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“…The images are manually annotated individually with bounding box annotations into eight categories of target road users, which include cars, pedestrians, buses, trucks, motors, vans, cyclists and parked bicycles. A compressed convolutional network (SVDet) is then constructed for transport object detection based on tensor train (TT) decomposition [15,16]. Compared with the baseline model RetinaNet [17] that represents the state-of-the-art in the relevant literature, SVDet achieves a mAP gain of 0.9%, while saving more than 68.8% of the parameters and 52.3% computational time.…”

Section: Introductionmentioning

confidence: 99%

Transport Object Detection in Street View Imagery Using Decomposed Convolutional Neural Networks

Bai,

Shang,

et al. 2023

Mathematics

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Deep learning has achieved great successes in performing many visual recognition tasks, including object detection. Nevertheless, existing deep networks are computationally expensive and memory intensive, hindering their deployment in resource-constrained environments, such as mobile or embedded devices that are widely used by city travellers. Recently, estimating city-level travel patterns using street imagery has been shown to be a potentially valid way according to a case study with Google Street View (GSV), addressing a critical challenge in transport object detection. This paper presents a compressed deep network using tensor decomposition to detect transport objects in GSV images, which is sustainable and eco-friendly. In particular, a new dataset named Transport Mode Share-Tokyo (TMS-Tokyo) is created to serve the public for transport object detection. This is based on the selection and filtering of 32,555 acquired images that involve 50,827 visible transport objects (including cars, pedestrians, buses, trucks, motors, vans, cyclists and parked bicycles) from the GSV imagery of Tokyo. Then a compressed convolutional neural network (termed SVDet) is proposed for street view object detection via tensor train decomposition on a given baseline detector. The method proposed herein yields a mean average precision (mAP) of 77.6% on the newly introduced dataset, TMS-Tokyo, necessitating just 17.29 M parameters and a computational capacity of 16.52 G FLOPs. As such, it markedly surpasses the performance of existing state-of-the-art methods documented in the literature.

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Section: Introductionmentioning

confidence: 99%