# Robust Deep Neural Object Detection and Segmentation for Automotive Driving Scenario with Compressed Image Data

Source: [Robust Deep Neural Object Detection and Segmentation for Automotive Driving Scenario with Compressed Image Data](<https://www.sota2.com/research/paper/68fa7fb21e209131cea9adb1>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T06:51:42.495Z

Authors: Kristian Fischer, Christian Blum, Christian Herglotz, Andr\\'e Kaup

Published: 2022-05-13T08:17:52.000Z

## Abstract

Deep neural object detection or segmentation networks are commonly trained with pristine, uncompressed data. However, in practical applications the input images are usually deteriorated by compression that is applied to efficiently transmit the data. Thus, we propose to add deteriorated images to the training process in order to increase the robustness of the two state-of-the-art networks Faster and Mask R-CNN. Throughout our paper, we investigate an autonomous driving scenario by evaluating the newly trained models on the Cityscapes dataset that has been compressed with the upcoming video coding standard Versatile Video Coding (VVC). When employing the models that have been trained with the proposed method, the weighted average precision of the R-CNNs can be increased by up to 3.68 percentage points for compressed input images, which corresponds to bitrate savings of nearly 48 %.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2205.06501>)

[PDF](<https://arxiv.org/pdf/2205.06501.pdf>)