Human-autonomy sensor fusion is an emerging technology with a wide range of applications, including object detection/recognition, surveillance, collaborative control, and prosthetics. For object detection, humans and computer-vision-based systems employ different strategies to locate targets, likely providing complementary information. However, little effort has been made in combining the outputs of multiple autonomous detectors and multiple human-generated responses. This paper presents a method for integrating several sources of human- and autonomy-generated information for rapid object detection tasks. Human electroencephalography (EEG) and button-press responses from rapid serial visual presentation (RSVP) experiments are fused with outputs from trained object detection algorithms. Three fusion methods—Bayesian, Dempster-Shafer, and Dynamic Dempster-Shafer—are implemented for comparison. Results demonstrate that fusion of these human classifiers with computer-vision-based detectors improves object detection accuracy over purely computer-vision-based detection (5% relative increase in mean average precision) and the best individual computer vision algorithm (28% relative increase in mean average precision). Computer vision fused with button press response and/or the XDAWN + Bayesian Linear Discriminant Analysis neural classifier provides considerable improvement, while computer vision fused with other neural classifiers provides little or no improvement. Of the three fusion methods, Dynamic Dempster-Shafer Theory (DDST) Fusion exhibits the greatest performance in this application.