To address the problems of high computational complexity, poor real-time performance, and misdetection and omission detection in the detection of small targets in unmanned aerial vehicle (UAV) aerial images by existing algorithms, an improved YOLOv8-based algorithm, named DySC-YOLOv8, is proposed for pollutant detection on building fa?ades. The proposed algorithm introduces a dynamic upsampler (DySample) and a spatial context-aware attention mechanism (SCAM). First, the DySample module replaces the conventional dynamic convolution with point sampling, which adaptively distributes the sampling points based on the image features and fine-tunes the distribution of the sampling points based on the dynamic range factor, thus effectively reducing the amount of model computation and improving real-time detection. Second, the SCAM module enhances the global information extraction capability, which allows the model to better integrate contextual information and further enhances the retention of key point information. Consequently, the accuracy of recognizing small targets in complex backgrounds as well as the perception of important features are enhanced. Finally, the SIoU loss function is used to reduce misjudgments when the prediction frame differs slightly from the actual target frame. Experimental results show that compared with the baseline model, the proposed algorithm improves the accuracy of the self-made dataset by 6.1 percentage points, while the parameters and floating-point operations are reduced by 9.97% and 9.88%, respectively. Meanwhile, validation experiments on the UAV-DT and DIOR datasets confirmed the good generalization and effectiveness of the proposed algorithm, thus demonstrating its usefulness for engineering applications.