Storage Tank Target Detection for Large-Scale Remote Sensing Images Based on YOLOv7-OT

Abstract

Since industrialization, global greenhouse gas emissions have gradually increased. Storage tanks, as industrial facilities for storing fossil energy, are one of the main sources of greenhouse gas emissions. Using remote sensing images to detect and locate storage tank targets over a large area can provide data support for regional air pollution prevention, control, and monitoring. Due to the circular terrain on the ground and the circular traces caused by human activities, the target detection model has a high false detection rate when detecting tank targets in large-scale remote sensing images. To address the above problems, a YOLOv7-OT model for tank target detection in large-scale remote sensing images is proposed. This model proposes a data pre-processing method of edge re-stitching for large-scale remote sensing images, which reduces the target loss caused by the edge of the image without losing the target information. In addition, to address the problem of small target detection, the CBAM is added to the YOLOv7 backbone network to improve the target detection accuracy under complex backgrounds. Finally, in response to the model’s misjudgment of targets during detection, a data post-processing method combining the spatial distribution characteristics of tanks is proposed to eliminate the misdetected targets. The model was evaluated on a self-built large-scale remote sensing dataset, the model detection accuracy reached 90%, and the precision rate reached 95.9%. Its precision rate and detection accuracy are better than those of the other three classic target detection models.

Keywords

• remote sensing
• storage tank
• target detection
• deep learning