Feature detectors are receiving increasing attention from computer vision research community, which have been widely utilized in a large number of applications, such as wide baseline matching, object recognition and categorization, image retrieval, visual search, robot localization and data mining.To discuss research challenges of investigation and directions for further research, analysis is made to the robustness and speed of current widely used feature detectors.Various feature detectors, including Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), Binary Robust Invariant Scalable Keypoints (BRISK), ORB, KAZE, and Accelerated-KAZE are reviewed.Also, the algorithms affecting robustness and speed of feature detectors are investigated based on steps of feature detection.The Mikolajczyk 05 testing image sequences are used to determine and analyze repeatability and time cost.The experimental results show that fast nonlinear scale space, Features from Accelerated Segment Test (FAST) and long-distance pairs iteration have a more comprehensive performance.