[1]ZHANG Qiang,HE Zicheng,SHENG Shoudong.Instrument panel detection method for oilfield injection station based on improved target detection model[J].CAAI Transactions on Intelligent Systems,2026,21(4):876-887.[doi:10.11992/tis.202508036]
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Instrument panel detection method for oilfield injection station based on improved target detection model

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 4 Page number: 876-887 Column: 学术论文—机器学习 Public date: 2026-07-05
Title:
Instrument panel detection method for oilfield injection station based on improved target detection model
Author(s):
ZHANG Qiang1;  HE Zicheng1;  SHENG Shoudong2
1. College of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China;
2. Institute of Geology, The Fifth Oil Production Plant of Daqing Oilfield Co., Ltd., Daqing 163513, China
Keywords:
small object detection; Faster R-CNN; dual branch fusion; mobile convolutional network; attention mechanism; lightweight network; low-light detection; edge device deployment
CLC:
TP391
DOI:
10.11992/tis.202508036
Abstract:
In the night operation environment of oilfield distribution stations, instrument panel target detection is limited by low light conditions, resulting in poor performance of traditional single-modal image detection methods in terms of accuracy and robustness. This paper proposes a mid-term fusion dual-branch target detection model based on improved Faster R-CNN, which aims to fuse the complementary information of visible light images and night vision images to improve the target detection performance in low-contrast environments at night. A dual-branch mid-term fusion strategy for visible and night vision images is designed to enhance the synergy of dual image features. By improving the lightweight mobile convolutional network as the backbone feature extraction network, the feature expression ability of key areas is effectively enhanced by combining the spatial attention mechanism. Experimental results on a custom oilfield distribution station instrument panel night image dataset show that this method achieves 88.92% and 46.87% in mAP@0.5 and mAP@0.5:0.95 indicators, respectively, which are 5.89% and 4.72% higher than the baseline Faster R-CNN model, respectively. It shows good detection accuracy and robustness in low-contrast night industrial scenes, while reducing the number of model parameters, making it easier to deploy on resource-limited edge devices.
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