Uncertainty estimation of the output from a deep neural network has recently become a hot topic, mainly due to Alex Kendall’s PhD thesis *Geometry and Uncertainty in Deep Learning for Computer Vision*, where he mentioned the uncertainty problems for semantic and geometrical problems in the computer vision field. What is more, it provides us a weighting strategy for the losses of multi-task learning, which is a very practical problem in both the academical and industrial fields. It is highly recommended to take a close look at Alex Kendall’s presentation of his PhD thesis Geometry and Uncertainty in Deep Learning. As a short introduction of Bayesian deep learning, you can also take a look at his blog article Bayesian deep learning - Alex Kendall.

Come to the topic today, the paper published in ICCV 2019 Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving tries to apply uncertainty estimation for the detection task, and it succeeded. Uncertainty estimation is much more than uncertainty output, it can also be used to construct a loss function and raise the detection accuracy. The paper focused on estimating the uncertainty of the bounding box localization, which means the coordinate of the bounding box center and its width and height. By using the uncertainty estimation, the paper managed to achieve 3.5 more mAP than the original YoloV3 with 1% more computational cost, what is more, the implementation is also quite straight forward. This blog will provide a in-depth explaination of this paper.