Object detection for nameplate based on neural architecture search

doi:10.11996/JG.j.2095-302X.2023040718

Abstract

Abstract:

In order to enhance the automation of building deep convolutional neural network (CNN) for object detection and further improve the detection accuracy, an improved DenseNAS-based neural architecture search method was proposed to automatically build a CNN for nameplate detection. First, the searchable subnet modules (CSP-Block1 and CSP-Block2) were designed to fuse deep and shallow layer feature mapping by enhancing the Head layer of DenseNAS. Subsequently, the search space was established based on the CSP-Block1 and CSP-Block2 to explore the Backbone and Head of CNN for nameplate detection. The experimental results demonstrated that the proposed method required about 9.35 GPU hours to search the optimal neural network on a nameplate dataset consisting of 5 classes, and that the detection accuracy mAP was about 97.3% on the test set, exceeding those of state-of-the-art methods, such as YOLOv5.

Key words: neural architecture search, convolutional neural network, CSP structure, nameplate, object detection

CLC Number:

TP391

DENG Wei-ming, YANG Tie-jun, LI Chun-chun, HUANG Lin. Object detection for nameplate based on neural architecture search[J]. Journal of Graphics, 2023, 44(4): 718-727.

Figures/Tables 22

References 26

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