  An object detection pipeline comprises a camera that captures the scene and
an object detector that processes these images. The quality of the images
directly affects the performance of the object detector. Many works nowadays
focus either on improving the image quality or improving the object detection
models independently, but neglect the importance of joint optimization of the
two subsystems. The goal of this paper is to tune the detection throughput and
accuracy of existing object detectors in the remote sensing scenario by
focusing on optimizing the input images tailored to the object detector. To
achieve this, we empirically analyze the influence of two selected camera
calibration parameters (camera distortion correction and gamma correction) and
five image parameters (quantization, compression, resolution, color model,
additional channels) for these applications. For our experiments, we utilize
three UAV data sets from different domains and a mixture of large and small
state-of-the-art object detector models to provide an extensive evaluation of
the influence of the pipeline parameters. Finally, we realize an object
detection pipeline prototype on an embedded platform for an UAV and give a best
practice recommendation for building object detection pipelines based on our
findings. We show that not all parameters have an equal impact on detection
accuracy and data throughput, and that by using a suitable compromise between
parameters we are able to achieve higher detection accuracy for lightweight
object detection models, while keeping the same data throughput.
