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ESOD:Edge-based Task Scheduling for Object Detection

arXiv 2110.11342 ↗First posted 20 Oct 2021cs.CV cs.LGcited by 0

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Abstract (arXiv)

Object Detection on the mobile system is a challenge in terms of everything. Nowadays, many object detection models have been designed, and most of them concentrate on precision. However, the computation burden of those models on mobile systems is unacceptable. Researchers have designed some lightweight networks for mobiles by sacrificing precision. We present a novel edge-based task scheduling framework for object detection (termed as ESOD). In detail, we train a DNN model (termed as pre-model) to predict which object detection model to use for the coming task and offloads to which edge servers by physical characteristics of the image task (e.g., brightness, saturation). The results show that ESOD can reduce latency and energy consumption by an average of 22.13% and 29.60% and improve the mAP to 45.8(with 0.9 mAP better), respectively, compared with the SOTA DETR model.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 26 references that are in the map. First posted on arXiv 20 Oct 2021.

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17 of 26 references.

References · 17

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
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  10. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
  11. 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,577
  12. 2017ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devicescited by 1,200
5 more
  1. 2018ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Designcited by 1,136
  2. 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,020
  3. 2020Sparse R-CNN: End-to-End Object Detection with Learnable Proposalscited by 495
  4. 2019CenterMask : Real-Time Anchor-Free Instance Segmentationcited by 201
  5. 2020Smart, Adaptive Energy Optimization for Mobile Web Interactionscited by 2

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