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.

中文翻译：

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用于水下物体检测和颜色转换联合学习的轻量级深度神经网络


水下图像处理已被证明在探索水下环境方面表现出巨大的潜力。它已应用于多种领域，例如水下地形扫描和自主水下航行器（AUV）驱动的应用，例如基于图像的水下物体检测。然而，由于人造光源的衰减、颜色失真和噪声以及可能的低端光学成像设备的影响，水下图像经常会出现退化。因此，目标检测性能将相应降低。为了解决这个问题，本文提出了一种轻量级的深层水下目标检测网络。关键是提出一种联合学习水下图像颜色转换和目标检测的深度模型。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，以较低的计算复杂度增强目标检测性能。我们在 Raspberry pi 平台上实现的实验结果证明了所提出的用于水下物体检测的轻量级联合学习模型与最先进的方法相比的有效性。