Comprehensive Analysis of the Object Detection Pipeline on UAVs

Abstract

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. Current works focus on independently improving the image quality or object detection models but neglect the importance of joint optimization of the two subsystems. This paper aims to tune the detection throughput and accuracy of existing object detectors in the remote sensing scenario by optimizing the input images tailored to the object detector. 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, and additional channels) for these applications. For our experiments, we utilize three Unmanned Aerial Vehicle (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 a 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. Using a suitable compromise between parameters, we can achieve higher detection accuracy for lightweight object detection models while keeping the same data throughput.