Finding a target quickly is one of the most important tasks in drone operations. In particular, rapid target detection is a critical issue for tasks such as finding rescue victims during the golden period, environmental monitoring, locating military facilities, and monitoring natural disasters. Therefore, in this study, an improved hierarchical probabilistic target search algorithm based on the collaboration of drones at different altitudes is proposed. This is a method for reducing the search time and search distance by improving the information transfer methods between high-altitude and low-altitude drones. Specifically, to improve the speed of target detection, a high-altitude drone first performs a search of a wide area. Then, when the probability of existence of the target is higher than a certain threshold, the search information is transmitted to a low-altitude drone which then performs a more detailed search in the identified area. This method takes full advantage of fast searching capabilities at high altitudes. In other words, it reduces the total time and travel distance required for searching by quickly searching a wide search area. Several drone collaboration scenarios that can be performed by two drones at different altitudes are described and compared to the proposed algorithm. Through simulations, the performances of the proposed algorithm and the cooperation scenarios are analyzed. It is demonstrated that methods utilizing hierarchical searches with drones are comparatively excellent and that the proposed algorithm is approximately 13% more effective than a previous method and much better compared to other scenarios.

Road traffic surveys determine the number and type of vehicles passing by a specific point over a certain period of time. The manual estimation of the number and type of vehicles from images captured by a camera is the most commonly used method. However, this method has the disadvantage of requiring high amounts of manpower and cost. Recently, methods of automating traffic volume surveys using sensors or deep learning have been widely attempted, but there is the disadvantage that a person must finally manually verify the data in order to ensure that they are reliable. In order to address these shortcomings, we propose a method for efficiently conducting road traffic volume surveys and obtaining highly reliable data. The proposed method detects vehicles on the road from CCTV (Closed-circuit television) images and classifies vehicle types using deep learning or a similar method. After that, it automatically informs the user of candidates with a high probability of error and provides a method for efficient verification. The performance of the proposed method was tested using a data set collected by an actual road traffic survey company. As a result, we proved that our method shows better accuracy than the previous method. The proposed method can reduce the labor and cost in road traffic volume surveys, and increase the reliability of the data due to more accurate results.

When searching for targets using unmanned aerial vehicles, speed is important for many applications such as the discovery of patients in a medical emergency. The speed of operation of actual unmanned aerial vehicles is strongly related to the performance of the camera sensor used for target recognition, search altitude, and the search algorithm employed by the unmanned aerial vehicle. In this study, the major factors affecting the speed of a probabilistic unmanned aerial vehicle target search are analyzed. In particular, simulations are performed to analyze the influence of the search altitude, sensor false alarm rate, and sensor missed detection rate on the required travel distance and the time required for a search. Furthermore, the search performance of an unmanned aerial vehicle is analyzed by varying the search altitude with fixed false alarm and missed detection probabilities. The simulation results show that the search performance is significantly affected by changes in the false alarm and missed detection probabilities of the sensor, and it confirms that the effect of the missed detection probability is greater than that of the false alarm probability. The second simulation proves that the altitude of an unmanned aerial vehicle is a very important factor for the speed of a target search. In particular, the result shows that, for a real data set, the search distance and time at 10 and 5 m are about 2.8 times and 14.3 times larger, respectively, than those at 20 m.