Since the Industrial Revolution, methane has become the second most important greenhouse gas component after CO₂ and the second most important culprit of global warming, leading to serious climate change problems such as droughts, fires, floods, and glacial melting. While most of the methane in the atmosphere comes from emissions from energy activities such as petroleum refining, storage tanks are an important source of methane emissions during the extraction and processing of crude oil and natural gas. Therefore, the use of high-resolution remote sensing image data for oil and gas production sites to achieve efficient and accurate statistics for storage tanks is important to promote the strategic goals of “carbon neutrality and carbon peaking”. Compared with traditional statistical methods for studying oil storage tanks, deep learning-based target detection algorithms are more powerful for multi-scale targets and complex background conditions. In this paper, five deep learning detection algorithms, Faster RCNN, YOLOv5, YOLOv7, RetinaNet and SSD, were selected to conduct experiments on 3568 remote sensing images from five different datasets. The results show that the average accuracy of the Faster RCNN, YOLOv5, YOLOv7 and SSD algorithms is above 0.84, and the F1 scores of YOLOv5, YOLOv7 and SSD algorithms are above 0.80, among which the highest detection accuracy is shown by the SSD algorithm at 0.897 with a high F1 score, while the lowest average accuracy is shown by RetinaNet at only 0.639. The training results of the five algorithms were validated on three images containing differently sized oil storage tanks in complex backgrounds, and the validation results obtained were better, providing more accurate references for practical detection applications in remote sensing of oil storage tank targets in the future.

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.