Object Detection in Hazy Environment Enhanced by Preprocessing Image Dataset with Synthetic Haze

Abstract

Object detection in hazy environment has always been a difficult task in the autonomous driving field. Huge breakthrough is hard to achieve due to the lack of large-scale hazy image dataset with detailed labels. In this work, we present a simple and flexible algorithm to generate synthetic haze to MS COCO training dataset, which aims to enhance the performance of object detection in haze when taking the new synthesized hazy images as training dataset. Our algorithm is inspired by the Multiple Linear Regression Dark Channel Prior (MLDCP), and we obtain a general model that can add synthetic haze to haze-free images by implementing Stochastic Gradient Descent (SGD) to the reversed MLDCP model. We further evaluate the mean average precision (mAP) of Mask R-CNN when we train the network with the Hazy-COCO training dataset and preprocessing test hazy dataset with existing single image dehazing algorithms.