Open Access

ARTICLE

LP-YOLO: Enhanced Smoke and Fire Detection via Self-Attention and Feature Pyramid Integration

Qing Long¹, Bing Yi², Haiqiao Liu^3,*, Zhiling Peng¹, Xiang Liu¹

1 Graduate School, Hunan Institute of Engineering, Xiangtan, 411104, China
2 College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, 411104, China
3 College of Electrical and Information Engineering, Hunan Institute of Engineering, Xiangtan, 411104, China

* Corresponding Author: Haiqiao Liu. Email:

(This article belongs to the Special Issue: Attention Mechanism-based Complex System Pattern Intelligent Recognition and Accurate Prediction)

Computers, Materials & Continua 2026, 86(3), 63 https://doi.org/10.32604/cmc.2025.072058

Received 18 August 2025; Accepted 03 November 2025; Issue published 12 January 2026

Abstract

Accurate detection of smoke and fire sources is critical for early fire warning and environmental monitoring. However, conventional detection approaches are highly susceptible to noise, illumination variations, and complex environmental conditions, which often reduce detection accuracy and real-time performance. To address these limitations, we propose Lightweight and Precise YOLO (LP-YOLO), a high-precision detection framework that integrates a self-attention mechanism with a feature pyramid, built upon YOLOv8. First, to overcome the restricted receptive field and parameter redundancy of conventional Convolutional Neural Networks (CNNs), we design an enhanced backbone based on Wavelet Convolutions (WTConv), which expands the receptive field through multi-frequency convolutional processing. Second, a Bidirectional Feature Pyramid Network (BiFPN) is employed to achieve bidirectional feature fusion, enhancing the representation of smoke features across scales. Third, to mitigate the challenge of ambiguous object boundaries, we introduce the Frequency-aware Feature Fusion (FreqFusion) module, in which the Adaptive Low-Pass Filter (ALPF) reduces intra-class inconsistencies, the offset generator refines boundary localization, and the Adaptive High-Pass Filter (AHPF) recovers high-frequency details lost during down-sampling. Experimental evaluations demonstrate that LP-YOLO significantly outperforms the baseline YOLOv8, achieving an improvement of 9.3% in mAP@50 and 9.2% in F1-score. Moreover, the model is 56.6% and 32.4% smaller than YOLOv7-tiny and EfficientDet, respectively, while maintaining real-time inference speed at 238 frames per second (FPS). Validation on multiple benchmark datasets, including D-Fire, FIRESENSE, and BoWFire, further confirms its robustness and generalization ability, with detection accuracy consistently exceeding 82%. These results highlight the potential of LP-YOLO as a practical solution with high accuracy, robustness, and real-time performance for smoke and fire source detection.

---

Keywords

---