基于THGS算法优化ResNet-18模型的图像识别

doi:10.13229/j.cnki.jdxbgxb.20230775

Abstract

Abstract:

This article proposes an improved THGS ResNet-18 recognition model for fast and accurate recognition of rice brown spot images. Firstly， apply Tent chaotic mapping to improve the hunger game search （HGS） algorithm， solving the problem of excessive randomness in the population initialization of the HGS algorithm. Secondly， the improved HGS algorithm hyperparameter is applied to optimize ResNet-18 model. Finally， the improved model THGS ResNet-18 was used to recognize 5 064 rice leaf images， and compared with other four ResNet-18 models improved by swarm intelligence algorithm for seven evaluation indicators. Experiments showed that the accuracy rate of the model proposed in this paper increased by 5.22~6.09 percentage points， sensitivity by 3.53~5.31 percentage points， specificity by 7.38 percentage points， precision by 6.95~7.13 percentage points， recall rate by 3.53~5.31 percentage points， f-measure by 5.22~6.20 percentage points， and g-mean by 5.24~6.13 percentage points.

Key words: deep learning, hunger game search algorithm, chaotic mapping, hyperparameter optimization, rice diseases

CLC Number:

TP399

Jian LI,Huan LIU,Yan-qiu LI,Hai-rui WANG,Lu GUAN,Chang-yi LIAO. Image recognition research on optimizing ResNet-18 model based on THGS algorithm[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(5): 1629-1637.

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指标	数值	指标	数值
ACC	96.05	Recall	94.60
Sensitivity	94.60	F-measure	95.89
Specificity	97.44	G-mean	96.01
Precision	97.22	-	-

模型指标	GA-ResNet-18	PSO-ResNet-18	GWO-ResNet-18	WOA-ResNet-18
ACC	89.96	90.39	90.39	90.83
Sensitivity	89.29	90.18	90.18	91.07
Specificity	90.60	90.60	90.60	90.60
Precision	90.09	90.18	90.18	90.27
Recall	89.29	90.18	90.18	91.07
F-measure	89.69	90.18	90.18	90.67
G-mean	89.94	90.39	90.39	90.83

Image recognition research on optimizing ResNet-18 model based on THGS algorithm

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