Underwater sensing and detection still rely heavily on acoustic equipment, known as sonar. As an imaging sonar, side-scan sonar can present a specific underwater situation in images, so the application scenario is comprehensive. However, the definition of side scan sonar is low; many objects are in the picture, and the scale is enormous. Therefore, the traditional image segmentation method is not practical. In addition, data acquisition is challenging, and the sample size is insufficient. To solve these problems, we design a semantic segmentation model of side-scan sonar images based on a convolutional neural network, which is used to realize the semantic segmentation of side-scan sonar images with few training samples. The model uses a large convolution kernel to extract large-scale features, adds a parallel channel using a small convolution kernel to obtain multi-scale features, and uses SE-block to focus on the weight of different channels. Finally, we verify the effect of the model on the self-collected side-scan sonar dataset. Experimental results show that, compared with the traditional lightweight semantic segmentation network, the model’s performance is improved, and the number of parameters is relatively small, which is easy to transplant to AUV.

Path planning is often considered as an important task in autonomous driving applications. Current planning method only concerns the knowledge of robot kinematics, however, in GPS denied environments, the robot odometry sensor often causes accumulated error. To address this problem, an improved path planning algorithm is proposed based on reinforcement learning method, which also calculates the characteristics of the cumulated error during the planning procedure. The cumulative error path is calculated by the map with convex target processing, while modifying the algorithm reward and punishment parameters based on the error estimation strategy. To verify the proposed approach, simulation experiments exhibited that the algorithm effectively avoid the error drift in path planning.

In the realm of underwater exploration, particularly within the domain of autonomous detection, sidescan sonar stands as a pivotal sensor apparatus. Autonomous detection models necessitate a substantial volume of scanned sonar image data for optimal training, yet the challenges and costs associated with acquiring such data pose significant limitations on the deployment of autonomous detection models in underwater exploration scenarios. Consequently, there arises a demand for the development of cost-effective data augmentation techniques. In the present investigation, an initial collection of scanned sonar image data was conducted during lake trials, encompassing diverse environmental regions, including rocky terrain, shadowed areas, and aquatic bodies. Subsequently, a proprietary generative adversarial network (GAN) model was devised for the purpose of synthesizing scanned sonar data. The synthesized data underwent denoising and underwent post-processing via algorithmic methods. Subsequently, similarity metrics were computed to gauge the quality of the generated scanned sonar data. Furthermore, a semantic segmentation model was meticulously crafted and trained by employing authentic data. The generated data were subsequently introduced into this semantic segmentation model. The output outcomes demonstrated that the model exhibited preliminary labeling proficiency on the generated image data, requiring only minimal manual intervention to conform to the standards of a conventional dataset. Following the inclusion of the labeled data into the original dataset and the subsequent training of the network model utilizing the expanded dataset, there was an observed discernible enhancement in the segmentation performance of the model.

In the realm of underwater environment detection, achieving information matching stands as a pivotal step, forming an indispensable component for collaborative detection and research in areas such as distributed mapping. Nevertheless, the progress in studying the matching of underwater side-scan sonar images has been hindered by challenges including low image quality, intricate features, and susceptibility to distortion in commonly used side-scan sonar images. This article presents a comprehensive overview of the advancements in underwater sonar image processing. Building upon the novel SchemaNet image topological structure extraction model, we introduce a feature matching model grounded in side-scan sonar images. The proposed approach employs a semantic segmentation network as a teacher model to distill the DeiT model during training, extracting the attention matrix of intermediate layer outputs. This emulates SchemaNet’s transformation method, enabling the acquisition of high-dimensional topological structure features from the image. Subsequently, utilizing a real side-scan sonar dataset and augmenting data, we formulate a matching dataset and train the model using a graph neural network. The resulting model demonstrates effective performance in side-scan sonar image matching tasks. These research findings bear significance for underwater detection and target recognition and can offer valuable insights and references for image processing in diverse domains.