A Survey of Target Detection and Recognition Methods in Underwater Turbid Areas

Abstract

Based on analysis of state-of-the-art research investigating target detection and recognition in turbid waters, and aiming to solve the problems encountered during target detection and the unique influences of turbidity areas, in this review, the main problem is divided into two areas: image degradation caused by the unique conditions of turbid water, and target recognition. Existing target recognition methods are divided into three modules: target detection based on deep learning methods, underwater image restoration and enhancement approaches, and underwater image processing methods based on polarization imaging technology and scattering. The relevant research results are analyzed in detail, and methods regarding image processing, target detection, and recognition in turbid water, and relevant datasets are summarized. The main scenarios in which underwater target detection and recognition technology are applied are listed, and the key problems that exist in the current technology are identified. Solutions and development directions are discussed. This work provides a reference for engineering tasks in underwater turbid areas and an outlook on the development of underwater intelligent sensing technology in the future.

Passive Visual Underwater Surveillance: A Survey

“…In the past, many surveys were written considering some particular or different aspect of underwater image analysis. Some of the past surveys are reported in (Schettini and Corchs (2010); Yang et al (2019); Wang et al (2019); Li et al (2020b); Soni and Kumare (2020); Fayaz et al (2020); Khurana and Tirpude (2020); Anwar and Li (2020); Jian et al (2021); Raveendran et al (2021); Mittal et al (2022); Xin Yuan (2022); Er (2022)). These surveys cover some particular aspects of underwater image analysis and rarely cover underwater surveillance modules.…”

“…Similarly, the articles Schettini and Corchs (2010); Wang et al (2019) discuss both underwater enhancement and restoration techniques. Only the surveys like Xin Yuan (2022) provide work on object detection for the underwater turbid area and Er (2022) cover the object detection part. However, the review presented in Xin Yuan (2022) is more specific to the underwater turbid areas and fish-type targets.…”

“…Only the surveys like Xin Yuan (2022) provide work on object detection for the underwater turbid area and Er (2022) cover the object detection part. However, the review presented in Xin Yuan (2022) is more specific to the underwater turbid areas and fish-type targets. Similarly, the review presented in Er (2022) describes deep learning algorithms for underwater object detection techniques.…”

Underwater Polarization Imaging Recovery Based on Polarimetric Residual Dense Network

“…In the past decade, machine learning and neural network have achieved a variety of successes in many areas including distance prediction [13], image dehazing [14], [15], visible light positioning (VLP) [16], and image recognition [17]. The deep learning method, as a sub-field of machine learning, has been recognized as an effective approach that leads to many technological advancements [18].…”

Underwater Image Enhancement in Epicontinental Sea Based on the Improved Algorithm of Dark Channel

“…Different from the deep sea, restoration and enhancement underwater images is a challenging problem owing to the ocean current disturbance in the epicontinental sea and the light propagation, absorption and scattering by micro suspended particles in epicontinental sea [5]. Special environment under epicontinental sea water provokes several combined degradation in images including color attenuation, blurring, low contrast, and their interaction (e.g., color distortion and haze effects) , these are important problems for underwater image enhancement [6][7][8][9][10].…”