The detection of urban appearance violation in unmanned aerial vehicle imagery faces several challenges. To address this problem, an optimized YOLOv8n-based urban appearance violation detection model is proposed. A custom dataset including four classes is created owing to the lack of a sufficient dataset. The Convolutional Block Attention Module attention mechanism is applied to improve the feature extraction ability of the model. A small target detection head is added to capture the characteristics of small targets and context information more effectively. The loss function Wise Intersection over Union is applied to improve the regression performance of the bounding box and the robustness of detection. Experimental results show that compared with the YOLOv8n model, the Precision, Recall, mAP_0.5, and mAP_0.5−0.95 of the optimized method increase by 3.8%, 2.1%, 3.3%, and 4.8%, respectively. Besides, an intelligent urban appearance violation detection system is developed, which generates and delivers warning messages via the WeChat official account platform.