Rethinking Image Restoration for Object Detection

Shangquan Sun, Wenqi Ren, Tao Wang, Xiaochun Cao

Keywords: Image Restoration, Object Detection, Image Dehazing, Low Light Enhancement, Targeted Adversarial Attack

TL;DR: We propose a training pipeline for image restoration model by generating pseudo ground truth with targeted adversarial attack, such that a subsequent detector can predicts better on its recovered image.

Abstract: Although image restoration has achieved significant progress, its potential to assist object detectors in adverse imaging conditions lacks enough attention. It is reported that the existing image restoration methods cannot improve the object detector performance and sometimes even reduce the detection performance. To address the issue, we propose a targeted adversarial attack in the restoration procedure to boost object detection performance after restoration. Specifically, we present an ADAM-like adversarial attack to generate pseudo ground truth for restoration training. Resultant restored images are close to original sharp images, and at the same time, lead to better results of object detection. We conduct extensive experiments in image dehazing and low light enhancement and show the superiority of our method over conventional training and other domain adaptation and multi-task methods. The proposed pipeline can be applied to all restoration methods and detectors in both one- and two-stage.