Background: Nowadays, small drones are inexpensive and can be purchased and used very easily. Unfortunately, they are also relatively easy to convert to weapons. As they become more widespread, these drones may become a serious security risk. One possible way to address this threat could be the early detection of small drones by using acoustic cameras. However, the question arises as to how good the detection performance of such cameras is, compared to that of a human observer. The goal of this project was to determine the acoustic detection-threshold of human observers for drones in the presence of ambient noise. Methods: Nineteen subjects volunteered to take part in the study. The constancy method was used to determine the detection threshold. During the test, the study participants were presented with a recording of a DJI Phantom2 Vision+ drone that varied in level in steps of 1dB over a range of 27dB around the estimated threshold value. The signals were superimposed by three different kinds of ambient noise which were presented in three successive test-runs. The subjects wore headphones over which they heard the ongoing ambient noise while they were presented with the drone sound at random intervals and levels. The test signal was on for 2 seconds during which the trial subject had to confirm the detection of the drone sound by pressing an assigned key on a notebook. Results: We’ve found detection thresholds for white noise, water or highway noise at -17dB, -18dB and -17dB respectively, expressed as level differences between test signal and noise. Comparison of our results with the detection performance of human observers in a simulated drone detection scenario, reproduced by loudspeakers in an anechoic chamber, showed good agreement. Further, it seems possible to assess the detection performance of an acoustic camera using our results.