Work

TCSN-YOLO: A Small-Target Object Detection Method for Fire Smoke

Year: 2025

Type: article

Abstract: Forest fires continue to pose a significant threat to public and personal safety. Detecting smoke in its early stages or when it is distant from the camera is challenging because it appears in only a small region of the captured images. This paper proposes a small-scale smoke detection algorithm called TCSN-YOLO to address these challenges. First, it introduces a novel feature fusion module called trident fusion (TF), which is innovatively designed and incorporated into the neck of the model. TF significantly enhances small target smoke recognition. Additionally, to obtain global contextual information with high computational efficiency, we propose a Cross Attention Mechanism (CAM). CAM captures diverse smoke features by assigning attention weights in both horizontal and vertical directions. Furthermore, we suggest using SoftPool to preserve more detailed information in the feature map. Normalized Wasserstein Distance (NWD) metric be embedded into the loss function of our detector to distinguish positive and negative samples under the same threshold. Finally, we evaluate the proposed model using AI For Humankind dataset and FlgLib dataset. The experimental results demonstrate that our method achieves 37.1% APs, 90.3% AP50, 40.4% AP50:95, 45.34 M Params and 170.5 G FLOPs.

Source: Fire

DOI: 10.3390/fire8120466

Authors: Cao Yang, Zhou Jun, Wen Hongyuan, Wang Gang

Institution: Taizhou Institute of Science and Technology

Language: English

Topic: Fire Detection and Safety Systems

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: National Natural Science Foundation of China