Multi-scale oil well detection in remote sensing images based on MSA-Net: Background separation and dynamic optimization strategy

Oil well detection is vital in remote sensing resource monitoring, significantly improving oilfield resource management and environmental protection. However, oil well targets pose challenges due to their small size, complex backgrounds, and diverse viewpoints. This paper proposes an innovative oil well detection framework to enhance detection accuracy and robustness. The framework combines multi-scale feature extraction, background separation, and enhancement techniques. First, the background separation module distinguishes oil well targets from the background, while the Generative Adversarial Network (GAN) enhances target saliency, reducing background interference. Then, a Feature Pyramid Network (FPN) is used for multi-scale feature extraction to handle various oil well target sizes and shapes, combined with an attention mechanism to optimize feature fusion for improved detection of small and partially occluded targets. Finally, an adaptive detection module adjusts the strategy based on target features, using bounding box regression and Non-Maximum Suppression (NMS) to refine results and ensure precise localization. Experimental results show significant improvements in performance, effectively addressing different target scales and occlusion issues, providing reliable support for oilfield management.