The proposed coarse-to-fine detection strategy can be extended to other object detection tasks by adapting the concept of multi-level processing to handle various challenges like detecting small and occluded objects in cluttered environments. For instance, in scenarios where objects are small and partially hidden, the coarse level can focus on identifying regions of interest based on objectness information, similar to how drones are localized in the provided context. This initial coarse detection can help narrow down the search space for the fine-grained detection stage, where more detailed features and context can be leveraged to precisely localize and classify objects. By incorporating object enhancement techniques and utilizing transformer networks for feature extraction and processing, the system can effectively address the challenges posed by small and occluded objects in cluttered environments.