Yeh Chia-Hung, Lin Chu-Han, Kang Li-Wei, Huang Chih-Hsiang, Lin Min-Hui, Chang Chuan-Yu, Wang Chua-Chin
IEEE Trans Neural Netw Learn Syst. 2022 Nov;33(11):6129-6143. doi: 10.1109/TNNLS.2021.3072414. Epub 2022 Oct 27.
Underwater image processing has been shown to exhibit significant potential for exploring underwater environments. It has been applied to a wide variety of fields, such as underwater terrain scanning and autonomous underwater vehicles (AUVs)-driven applications, such as image-based underwater object detection. However, underwater images often suffer from degeneration due to attenuation, color distortion, and noise from artificial lighting sources as well as the effects of possibly low-end optical imaging devices. Thus, object detection performance would be degraded accordingly. To tackle this problem, in this article, a lightweight deep underwater object detection network is proposed. The key is to present a deep model for jointly learning color conversion and object detection for underwater images. The image color conversion module aims at transforming color images to the corresponding grayscale images to solve the problem of underwater color absorption to enhance the object detection performance with lower computational complexity. The presented experimental results with our implementation on the Raspberry pi platform have justified the effectiveness of the proposed lightweight jointly learning model for underwater object detection compared with the state-of-the-art approaches.
水下图像处理已被证明在探索水下环境方面具有巨大潜力。它已应用于广泛的领域，如水下地形扫描以及自主水下航行器（AUV）驱动的应用，如基于图像的水下目标检测。然而，水下图像常常由于衰减、颜色失真、来自人工光源的噪声以及可能的低端光学成像设备的影响而退化。因此，目标检测性能也会相应下降。为了解决这个问题，本文提出了一种轻量级的深度水下目标检测网络。关键在于提出一个深度模型，用于联合学习水下图像的颜色转换和目标检测。图像颜色转换模块旨在将彩色图像转换为相应的灰度图像，以解决水下颜色吸收问题，从而以较低的计算复杂度提高目标检测性能。我们在树莓派平台上实现的实验结果证明，与现有最先进的方法相比，所提出的用于水下目标检测的轻量级联合学习模型是有效的。
