Objective To address issues such as excessive dust at the material unloading point in the yard and the inability of spray systems to quickly and accurately locate and remove the dust this paper proposed a detection method integrating a context-aware module to effectively detect on-site dust and assist the high-pressure spray system in rapid and precise dust removal. Methods The backbone network of the model was a lightweight network EfficientNetB0 which significantly reduced model parameters while achieving efficient feature extraction and improving deployment efficiency. Additionally, the CoT Contextual Transformer module was used to explore contextual information between adjacent layers enhancing self-attention learning with a combination of static and dynamic information to improve feature extraction and expression. Finally after channel adjustment and fusion between three output layers the input was passed to the Adaptive Spatial Feature Fusion ASFF network for further integration of information features across channels so as to facilitate the learning of feature details. Results The total model size of this method is 20. 42MB facilitating model embedding usage and the mean Average Precision mAP is 95. 98%. Conclusion The proposed context-aware module integrated detection method reduces computational load and has a certain advantage in accuracy for detecting dust at the material unloading point in the yard meeting detection requirements effectively.