Artificial reef detection in multibeam sonar images is an important measure for the monitoring and assessment of biological resources in marine ranching. With respect to how to accurately detect artificial reefs in multibeam sonar images, this paper proposes an artificial reef detection framework for multibeam sonar images based on convolutional neural networks (CNN). First, a large-scale multibeam sonar image artificial reef detection dataset, FIO-AR, was established and made public to promote the development of artificial multibeam sonar image artificial reef detection. Then, an artificial reef detection framework based on CNN was designed to detect the various artificial reefs in multibeam sonar images. Using the FIO-AR dataset, the proposed method is compared with some state-of-the-art artificial reef detection methods. The experimental results show that the proposed method can achieve an 86.86% F1-score and a 76.74% intersection-over-union (IOU) and outperform some state-of-the-art artificial reef detection methods.

Marine geophysical and geological investigations are crucial for evaluating the construction suitability of artificial fish reefs (AFRs). Key factors such as seabed topography, geomorphology, sub-bottom structure, and sediment type significantly influence AFR design and site selection. Challenges such as material sinking, sediment instability, and scouring effects should be critically considered and addressed in the construction of AFR, particularly in areas with soft mud or dynamic environments. In this study, detailed investigations were conducted approximately seven months after the deployment of reef materials in the AFR experimental zones around Xiaoguan Island, located in the western South Yellow Sea, China. Based on morphological factors, using data from multibeam echosounders and side-scan sonar, the study area was divided into three geomorphic zones, namely, the tidal flat (TF), underwater erosion-accumulation slope (UEABS), and inclined erosion-accumulation shelf plain (IEASP) zones. The focus of this study was on the UEABS and IEASP experimental zones, where reef materials (concrete or stone blocks) were deployed seven months earlier. The comprehensive interpretation results of multi-source high-resolution acoustic images showed that the average settlement of individual reefs in the UEABS experimental zone was 0.49 m, and their surrounding seabed experienced little to no scouring. This suggested the formation of an effective range and height, making the zone suitable for AFR construction. However, in the IEASP experimental zone, the seabed sediment consisted of soft mud, causing the reef materials to sink into the seabed after deployment, preventing the formation of an effective range and height, and rendering the area unsuitable for AFR construction. These findings provided valuable scientific guidance for AFR construction in the study area and other similar coastal regions.

The topographic features of the seafloor can be observed clearly via high-resolution side-scan sonar imagery. However, the faithful interpretation of a sonar image depends strongly on the accuracy with which the location of the sea bottom line can be tracked within the image, and current tracking methods function poorly under high sonar signal noise or suffer from high complexity. The present work addresses this issue by applying the characteristics of simple sonar waterfall maps in conjunction with robust edge detection and multi-scale analysis based on wavelet transform modulus maxima. The proposed tracking method is demonstrated to provide superior effectiveness and accuracy in comparison with existing baseline methods based on the results of experiments conducted with a representative side-scan sonar image with and without applied speckle noise. This superiority can be attributed to the good localization characteristics and multi-scale detection features of wavelet transform analysis, which can suppress the impact of noise in the sonar image on the accurate extraction of edge information.