A Lightweight Pedestrian Detection Model for Edge Computing Systems

Chen, Wenhui; Kuo, Han-Yang; Lin, Yu‐Chen; Tsai, Cheng-Han

doi:10.1007/978-3-030-53036-5_11

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…This may lead to the assumption that they increased accuracy by decreasing speed, which is acceptable for AV use cases. On the opposite approach, [4,54] provides an increment of speed, although the accuracy is not given which also leads to the assumption that they tip the balance in favour of the speed by sacrificing the accuracy.…”

Section: Pedestrian Detectionmentioning

confidence: 99%

A dynamic discarding technique to increase speed and preserve accuracy for YOLOv3

Martinez‐Alpiste

Golkarnarenji

Wang

et al. 2021

Neural Comput & Applic

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This paper proposes an acceleration technique to minimise the unnecessary operations on a state-of-the-art machine learning model and thus to improve the processing speed while maintaining the accuracy. After the study of the main bottlenecks that negatively affect the performance of convolutional neural networks, this paper designs and implements a discarding technique for YOLOv3-based algorithms to increase the speed and maintain accuracy. After applying the discarding technique, YOLOv3 can achieve a 22% of improvement in terms of speed. Moreover, the results of this new discarding technique were tested on Tiny-YOLOv3 with three output layers on an autonomous vehicle for pedestrian detection and it achieved an improvement of 48.7% in speed. The dynamic discarding technique just needs one training process to create the model and thus execute the approach, which preserves accuracy. The improved detector based on the discarding technique is able to readily alert the operator of the autonomous vehicle to take the emergency brake of the vehicle in order to avoid collision and consequently save lives.

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Section: Pedestrian Detectionmentioning

confidence: 99%