Lightweight Flame Detection Algorithm Based on Improved RT-DETR

doi:10.3969/j.issn.1006-2475.2025.07.015

Abstract

Abstract:
Abstract: In order to improve the accuracy of flame detection and make the model lighter， a lightweight RT-DETR flame detection algorithm is proposed. First， EfficientVit is selected as the feature extraction network to reduce model computation and complexity. Secondly， an efficient hybrid encoder is designed to reduce the number of model parameters and the amount of calculation while maintaining the detection accuracy. The encoder consists of the LPE-AIFI module， which focuses on processing deep features， and the CGAFusion module， which improves the detection capability of the model through multi-scale feature fusion. Finally， the boundary box regression loss function MDPIoU is introduced to further improve the accuracy of the algorithm. The experimental results show that the floating-point operations （FLOPs） of the improved model are reduced by 48.8% and the number of parameters by 43.4% compared with the original model. On the basis of lightweight， mAP@0.5 reaches 88.6% and mAP@0.5:0.95 reaches 67.4%， which are respectively 2.2 percentage points and 2.7 percentage points higher than the benchmark model.

Key words: Key words: flame detection, RT-DETR, EfficientViT, lightweight, multi-scale feature fusion

CLC Number:

中图分类号：TP391.4

WU Dong, FAN Yongsheng, SAN Binbin. Lightweight Flame Detection Algorithm Based on Improved RT-DETR[J]. Computer and Modernization, 2025, 0(07): 106-111.

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