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Object detection with Unmanned Aerial Vehicles (UAVs) has attracted muchattention in the research field of computer vision. However, not easy toaccurately detect objects with data obtained from UAVs, which capture imagesfrom very high altitudes, making the image dominated by small object sizes,that difficult to detect. Motivated by that challenge, we aim to improve theperformance of the one-stage detector YOLOv3 by adding a Spatial PyramidPooling (SPP) layer on the end of the backbone darknet-53 to obtain moreefficient feature extraction process in object detection tasks with UAVs. Wealso conducted an evaluation study on different versions of YOLOv3 methods.Includes YOLOv3 with SPP, YOLOv3, and YOLOv3-tiny, which we analyzed with theVisDrone2019-Det dataset. Here we show that YOLOv3 with SPP can get results mAP0.6% higher than YOLOv3 and 26.6% than YOLOv3-Tiny at 640x640 input scale andis even able to maintain accuracy at different input image scales than otherversions of the YOLOv3 method. Those results prove that the addition of SPPlayers to YOLOv3 can be an efficient solution for improving the performance ofthe object detection method with data obtained from UAVs.

YOLOv3 with Spatial Pyramid Pooling for Object Detection with Unmanned Aerial Vehicles