Cone Detection

Cone detection encompasses a range of research efforts focused on identifying and classifying cone-shaped objects in diverse contexts, from Martian geological features to traffic cones in road scenes. Current research emphasizes developing efficient algorithms, often leveraging machine learning models like YOLOv5 and adapting optimization techniques such as cone-aligned vector estimation and decomposition-based evolutionary algorithms, to improve accuracy and speed. These advancements are crucial for applications spanning planetary science (e.g., automated geological mapping) and computer vision (e.g., autonomous driving safety), where robust and rapid cone detection is essential. The development of benchmark datasets, like ConeQuest, is also driving progress by providing standardized evaluation metrics for algorithm comparison.