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<p>Accurate storage tank detection in remote sensing images is vital for monitoring methane emissions, a potent greenhouse gas, from the oil and gas industry. Existing methods, such as traditional geometric and spectral feature-based approaches, suffer from high false detection rates due to background variations and imaging conditions, while deep learning models like YOLO series and EfficientDet struggle with small objects, multi-scale features, background interference, and regression sensitivity, leading to missed detections and false positives. This study introduces a novel method integrating circular bounding boxes and a Large Selective Kernel (LSK) to address these gaps. Circular bounding boxes, aligned with storage tanks’ typical circular shape, stabilize Intersection over Union (IoU) for small objects, while LSK dynamically adjusts the receptive field to leverage contextual information effectively. Implemented on a YOLO-v10 framework and evaluated on a comprehensive dataset comprising DIOR, NWPUU_RESISC45, NWPU VHR-10, TGRS-HRRSD, and a self-built dataset (totaling 3568 images and 46075 storage tanks), our approach achieved a precision of 0.911, recall of 0.902, and mean Average Precision (mAP@0.5) of 0.931. These results represent improvements of 2.0% in precision, 2.7% in recall, and 1.8% in mAP@0.5 over the state-of-the-art YOLO-v10 baseline, offering a robust tool for pinpointing methane emission sources and supporting environmental sustainability efforts in the oil and gas sector.</p>

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Storage tank detection in remote sensing images based on circular bounding boxes and large selective kernel

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月18日

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  • Yu Liu,
  • Yong Wan,
  • Weimin Huang,
  • Zihao Zhan

摘要

Accurate storage tank detection in remote sensing images is vital for monitoring methane emissions, a potent greenhouse gas, from the oil and gas industry. Existing methods, such as traditional geometric and spectral feature-based approaches, suffer from high false detection rates due to background variations and imaging conditions, while deep learning models like YOLO series and EfficientDet struggle with small objects, multi-scale features, background interference, and regression sensitivity, leading to missed detections and false positives. This study introduces a novel method integrating circular bounding boxes and a Large Selective Kernel (LSK) to address these gaps. Circular bounding boxes, aligned with storage tanks’ typical circular shape, stabilize Intersection over Union (IoU) for small objects, while LSK dynamically adjusts the receptive field to leverage contextual information effectively. Implemented on a YOLO-v10 framework and evaluated on a comprehensive dataset comprising DIOR, NWPUU_RESISC45, NWPU VHR-10, TGRS-HRRSD, and a self-built dataset (totaling 3568 images and 46075 storage tanks), our approach achieved a precision of 0.911, recall of 0.902, and mean Average Precision (mAP@0.5) of 0.931. These results represent improvements of 2.0% in precision, 2.7% in recall, and 1.8% in mAP@0.5 over the state-of-the-art YOLO-v10 baseline, offering a robust tool for pinpointing methane emission sources and supporting environmental sustainability efforts in the oil and gas sector.