Dual-Channel and Two-Stage Dehazing Network for Promoting Ship Detection in Visual Perception System

Abstract

Maritime video surveillance of visual perception system has become an essential method to guarantee unmanned surface vessels (USV) traffic safety and security in maritime applications. However, when visual data are collected in a foggy marine environment, the essential optical information is often hidden in the fog, potentially resulting in decreased accuracy of ship detection. Therefore, a dual-channel and two-stage dehazing network (DTDNet) is proposed to improve the clarity and quality of the image to guarantee reliable ship detection under foggy conditions. Specifically, an upper and lower sampling structure is introduced to expand the original two-stage dehazing network into a two-channel network, to further capture the image features from different scale. Meanwhile, the attention mechanism is combined to provide different weights for different feature maps to maintain more image information. Furthermore, the perceptual function is constructed with the MSE-based loss function, so that it can better reduce the gap between the dehazing image and the unhazy image. Extensive experiments show that DTDNet has a better dehazing performance on both visual effects and quantitative index than other state-of-the-art dehazing networks. Moreover, the dehazing network is combined with the problem of ship detection under a sea-fog environment, and experiment results demonstrate that our network can be effectively applied to improve the visual perception performance of USV.