D[formula omitted]-Net: Integrated multi-task convolutional neural network for water surface deblurring, dehazing and object detection

Abstract

Visual perception is one of the key technologies for smart ships to achieve intelligent navigation in various complex water scenes, which adopts the object detection algorithm based on visible image to detect obstacles. Affected by the rolling and high-speed motion of ships and bad weather, motion blur and haze appear in the images of vision sensor, which seriously interfere with the obstacle detection. To ensure the navigation safety of smart ships, we propose the integrated multi-task deblurring, dehazing and object detection convolutional neural network (D3-Net). Firstly, the proposed method is able to complete deblurring, dehazing and object detection within a single network, which is more suitable to be applied in intelligent navigation. Secondly, different from traditional methods that reconstruct enhanced images first and then perform object detection, D3-Net combines image reconstruction and detection feature extraction to realize the integration of image enhancement and detection procedure. Finally, to improve the deblurring and dehazing performance of the network, we propose classification attention-based detection feature loss (CADF-Loss), which uses the image classification features to guide the network perform the detection feature enhancement more specifically. The experimental results in real navigation scenes show that D3-Net not only improves the detection accuracy of blurred and hazy images by 42%, but also has excellent detection efficiency, which is helpful to improve the navigation safety performance of smart ships.