Detecting multiobjects in a wide-area scenario efficiently is a critical technology for industry and security applications. The detection performance has benefited enormously from the probability estimation of the unknown environment. Representative methods like the particle filter (PF) construct the probability distribution model and iteratively locate target objects. However, randomly sampling and detecting image patches in the large covering field-of-view (FOV) make these methods inefficient and computation costly. To address these issues, we propose a region probability map (RPM) guided fast wide-area detection system that can simultaneously detect multiobjects from a large FOV at 300 frames per second (fps) through a coarse-to-fine grained detection paradigm. Specifically, a segmentation-based RPM generation module is introduced to assign probability measurements to different regions of the panoramic image, which models how likely the desired objects will occur in these regions. Then, based on the generated probability map of the whole scene, a novel RPM-guided PF framework is proposed to speed up the detection process by concentrating the detection on high-probability areas. Finally, a rapid and low-latency active detection system based on a wide-angle camera, a high-speed camera, and an ultrafast galvano-mirror is implemented, which gains a 15.38% efficiency improvement while achieves more accurate detection compared with existing methods. Extensive experimental results verify the robustness and effectiveness of our proposed system.