Article: HKB-YOLO: transmission line fire detection method based on hierarchical feature fusion Journal: International Journal of Security and Networks (IJSN) 2024 Vol.19 No.4 pp.188 - 198 Abstract: Timely transmission line fire inspections are vital for power system safety. Although deep learning models are widely used for flame detection, struggle with small target recognition due to background interference and are vulnerable to input data perturbations, posing security risks. This study introduces a hierarchical feature fusion method based on the you only look once Version 8 (YOLOV8) framework. It employs high-performance GPU network version 2 (HGNetV2) to enhance small-target feature extraction while reducing computational complexity. A spatial pyramid pooling-fast module with large separable kernel attention is designed to highlight key features and suppress background noise. The proposed bidirectional slim neck structure (BiSlimneck) reduces feature loss. Adversarial training enhances the model's robustness. Experimental results show a 1.4% accuracy improvement, with a reduction in parameters and complexity by 18.3% and 21.6%, respectively. After adversarial training, the accuracy of the network in the face of attacks increased by 10% compared to the pre-training. Inderscience Publishers - linking academia, business and industry through research

Title: HKB-YOLO: transmission line fire detection method based on hierarchical feature fusion

Authors: Zhilei Ying; Yanan Meng; Ruoxi Chen; Jianlou Lou

Addresses: School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, 132022, China ' Department of Automation, Jilin Institute of Chemical Technology, Jilin, 132022, China ' School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, 132022, China ' School of Computer Engineering, Northeast Electric Power University, Jilin, 132012, China

Abstract: Timely transmission line fire inspections are vital for power system safety. Although deep learning models are widely used for flame detection, struggle with small target recognition due to background interference and are vulnerable to input data perturbations, posing security risks. This study introduces a hierarchical feature fusion method based on the you only look once Version 8 (YOLOV8) framework. It employs high-performance GPU network version 2 (HGNetV2) to enhance small-target feature extraction while reducing computational complexity. A spatial pyramid pooling-fast module with large separable kernel attention is designed to highlight key features and suppress background noise. The proposed bidirectional slim neck structure (BiSlimneck) reduces feature loss. Adversarial training enhances the model's robustness. Experimental results show a 1.4% accuracy improvement, with a reduction in parameters and complexity by 18.3% and 21.6%, respectively. After adversarial training, the accuracy of the network in the face of attacks increased by 10% compared to the pre-training.

Keywords: fire detection; small target detection; YOLOV8; feature fusion; attention mechanism.

DOI: 10.1504/IJSN.2024.143773

International Journal of Security and Networks, 2024 Vol.19 No.4, pp.188 - 198

Received: 08 Oct 2024
Accepted: 27 Oct 2024
Published online: 06 Jan 2025 *

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Title: HKB-YOLO: transmission line fire detection method based on hierarchical feature fusion

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