基于显著性先验和金字塔转换机制的Trans-Sal Det小目标油井检测模型OA

To address the difficulty of accurately detecting small oil well targets in oilfield remote sensing images under complex surface environments and background interference,this paper proposes a Trans-Sal Det small object detection model that integrates saliency priors with a pyramid transformation mech-anism.A saliency generation module based on Tiny-U-Net is constructed to produce high-resolution sa-liency maps,highlighting potential oil well regions while suppressing redundant background informa-tion.The saliency maps are concatenated with the original remote sensing images at the channel level and fed into a pyramid Transformer encoder.Through a multi-scale window self-attention mechanism,cross-layer feature modeling is achieved,effectively integrating low-level detailed features with high-lev-el semantic information.A cross-scale feature fusion strategy is introduced to enhance the model's rep-resentation capability for oil well targets of different scales.Comparative and ablation experiments are conducted on typical oilfield remote sensing datasets to systematically validate the model performance.The results demonstrate that Trans-Sal Det exhibits superior small object detection performance in com-plex backgrounds,occlusion,and low-contrast scenarios,with significantly improved recall and detec-tion accuracy compared to mainstream methods.The introduction of saliency priors effectively guides at-tention to focus on key regions,enhancing the Transformer's perception of small targets.The proposed method provides an efficient and feasible technical approach for automated oilfield remote sensing detec-tion and intelligent monitoring,with strong potential for practical applications.