Optimized faster R-CNN for oil wells detection from high-resolution remote sensing images

As the oil and gas industry is crucial to the global energy market, policymakers need accurate information about local oil reserves and the harmful environmental effects of drilling, such as damage to public land and wildlife. Therefore, accurate automatic detection of the location, distribution and quantity of oil wells is essential. Recent advancements in remote sensing and deep learning technologies provide potential solutions for automatic oil wells detection using high-resolution remote sensing images. This study proposes an optimized Faster R-CNN-based model that incorporates three key modifications to improve the accuracy of oil wells detection. The modifications include replacing the VGG16 network with the ResNet50 network to improve the model's feature extraction capabilities, substituting the ordinary convolution of ResNet with a dilated convolution to improve the model's receptive field, and constructing a feature pyramid to improve the model's ability to detect small targets and objects at different scales. Also, an edge detection module is added to further improve the detection accuracy. Furthermore, a new framework based on Faster R-CNN and leveraging Soft-NMS (Non-Maximum Suppression) and the proposed ClusterRPN sub-network is combined to address the problem of clustered oil wells detection. Experimental results demonstrate that the proposed optimized model outperforms existing models.