@article{Cao2026, 
author = {Qian-Wen Cao and Jin-Rong Ma and Lai-Bin Zhang},
title = {MSA-DETR: Multi-scale attention enhanced DETR for object detection in oilfield surveillance},
year = {2026},
journal = {Petroleum Science},
volume = {23},
number = {5},
pages = {2686-2697},
keywords = {Object detection, Petroleum drilling safety, Multi-scale perception, Drilling engineering, Artificial intelligence},
url = {https://www.sciopen.com/article/10.1016/j.petsci.2026.03.045},
doi = {10.1016/j.petsci.2026.03.045},
abstract = {In modern petroleum engineering, ensuring operational safety at drilling sites is of critical importance. Visual object detection plays a key role in intelligent safety monitoring systems by enabling real-time supervision of personnel and equipment. However, safety-critical targets in drilling scenes are often small, partially occluded, and embedded in cluttered environments, leading to decreased detection accuracy and potential safety risks. Existing convolutional neural networks (CNN)-based detectors, although effective in natural scenes, often exhibit limited robustness under such complex industrial conditions. To address these challenges, this paper proposes MSA-DETR, a Transformer-based detection framework designed to enhance multi-scale perception in drilling monitoring scenarios. By improving the ability to capture both global contextual information and fine-grained visual cues, the proposed approach enhances sensitivity to safety-relevant objects. Extensive experiments conducted on two real-world drilling monitoring datasets demonstrate that MSA-DETR consistently outperforms state-of-the-art detection methods, providing more reliable visual perception for petroleum safety management and accident prevention.}
}