IFS-DETR: A real-time industrial fire smoke detection algorithm based on an end-to-end structured network

Fire prevention in industrial settings are paramount for ensuring human safety and economic stability. However, current mainstream DETR detectors face significant challenges in applications due to the necessity for extensive memory accesses and inference delays. To tackle these complexities, we propose an Industrial Fire Smoke Detector based on an end-to-end structured framework. In a series of innovative optimizations, we firstly adopt a more lightweight backbone network, LeanNet, for feature extraction. Combined with the optimized Transformer architecture, this approach enhances the model’s detection speed to address real-time challenges effectively. Secondly, we introduce a Feature Fusion Network based on alignment mechanisms to enhance the DETR model’s multi-scale object representation capabilities without significantly increasing latency. Subsequently, to facilitate easier training and optimization of IFS-DETR, we introduce IoU-aware query selection and an aspect ratio-based denoising training strategy, and enhance the localization loss function using Inner-SIoU. Finally, we deploy IFS-DETR on NVIDIA Jetson Orin Nano. The dataset is available at https://github.com/Sonnenb1ume/IFS-DETR.