[1]XU Weifeng,LEI Yao,WANG Hongtao,et al.Research on object detection models for edge devices[J].CAAI Transactions on Intelligent Systems,2025,20(4):871-881.[doi:10.11992/tis.202406015]
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Research on object detection models for edge devices

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 4 Page number: 871-881 Column: 学术论文—机器学习 Public date: 2025-08-05
Title:
Research on object detection models for edge devices
Author(s):
XU Weifeng1; 2;  LEI Yao1;  WANG Hongtao1; 2;  ZHANG Xu1
1. Department of Computer, North China Electric Power University(Baoding), Baoding 071003, China;
2. Hebei Key Laboratory of Knowledge Computing for Energy & Power, Baoding 071003, China
Keywords:
object detection; YOLO; edge devices; inference accuracy; inference speed; data read/write volume; computational load; model deployment
CLC:
TP391.4
DOI:
10.11992/tis.202406015
Abstract:
Existing object detection models can be improved in terms of balancing detection performance and inference speed on edge devices. Hence, a YOLO (you can only look once) v8-based model optimized for various edge devices is proposed. In the Backbone, an EC2f (extended coarse-to-fine) structure is designed to reduce parameters, computation, and data read/write volume. In the Neck, the YOLOv6-3.0 version is used to accelerate inference while maintaining accuracy. In the Head, a multiscale convolutional detection head, which further reduces computational load and complexity, is featured. Two versions (n/s scales) are designed to suit different edge devices. Experiments on an X-ray dataset demonstrate that the proposed model improves inference accuracy by 0.5%/1.7% and speed by 11.6%/11.2% compared with baseline models of the same scale. Generalization tests on other datasets present an increase in inference speed of over 10% and an accuracy reduction controlled within 1.3%. Overall, the model achieves a satisfactory balance between inference accuracy and speed.
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