Work

Task-oriented RT-DETR adaptation for minute cigarette detection in complex laboratory scenes

Year: 2026

Type: article

Abstract: Detecting minute cigarettes in complex laboratory surveillance scenes is challenging because of their small scale, partial occlusion, weak visual cues, cluttered backgrounds, and sensitivity to bounding-box deviations. This study develops a task-oriented adaptation of the Real-Time Detection Transformer (RT-DETR). Receptive Field Attention Convolution (RFAConv) is integrated into the ResNet-18 backbone, High-Low Frequency Attention (HiLo) is applied to high-level intra-scale interaction, and the existing Inner-MPDIoU formulation replaces GIoU as the intersection-over-union-based regression objective. Across three training runs, the complete model achieves an mAP@50 of $$89.6 \pm 0.4$$ % and an mAP@50:95 of $$50.2 \pm 0.2$$ %, compared with $$82.9 \pm 0.4$$ % and $$45.1 \pm 0.5$$ % for RT-DETR-R18. On an independent 150-image hold-out set, it achieves $$85.8 \pm 0.2$$ % mAP@50 and $$46.2 \pm 0.1$$ % mAP@50:95, exceeding the baseline values of $$77.2 \pm 0.2$$ % and $$41.7 \pm 0.3$$ %. In batch-one end-to-end inference on an NVIDIA GeForce RTX 4060 Ti, the model achieves 18 ms per frame and 56 FPS, with 3.4 GB peak GPU-memory occupancy. Although computation increases from 58.3 to 98.3 GFLOPs, the results demonstrate improved detection and hold-out performance while retaining practical workstation-level inference efficiency.

Source: Scientific Reports

DOI: 10.1038/s41598-026-75001-5

Authors: Jinrong Hao, Guangyu Shi, Xufeng Li, Xiaoran Cui, Xingjian Wang

Institution: Northeast Forestry University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,690

Funder: Natural Science Foundation of Heilongjiang Province