Search Results (2)

In order to effectively respond to floods and water emergencies that result in the drowning of missing persons, timely and effective search and rescue is a very critical step in underwater rescue. Due to the complex underwater environment and low visibility, unmanned underwater vehicles (UUVs) with sonar are more efficient than traditional manual search and rescue methods to conduct active searches using deep learning algorithms. In this paper, we constructed a sound-based rescue target dataset that encompasses both the source and target domains using deep transfer learning techniques. For the underwater acoustic rescue target detection of small targets, which lack image feature accuracy, this paper proposes a two-branch convolution module and improves the YOLOv5s algorithm model to design an acoustic rescue small target detection algorithm model. For an underwater rescue target dataset based on acoustic images with a small sample acoustic dataset, a direct fine-tuning using optical image pre-training lacks cross-domain adaptability due to the different statistical properties of optical and acoustic images. This paper therefore proposes a heterogeneous information hierarchical migration learning method. For the false detection of acoustic rescue targets in a complex underwater background, the network layer is frozen during the hierarchical migration of heterogeneous information to improve the detection accuracy. In addition, in order to be more applicable to the embedded devices carried by underwater UAVs, an underwater acoustic rescue target detection algorithm based on ShuffleNetv2 is proposed to improve the two-branch convolutional module and the backbone network of YOLOv5s algorithm, and to create a lightweight model based on hierarchical migration of heterogeneous information. Through extensive comparative experiments conducted on various acoustic images, we have thoroughly validated the feasibility and effectiveness of our method. Our approach has demonstrated state-of-the-art performance in underwater search and rescue target detection tasks. Full article

Underwater acoustic target recognition (UATR) technology has been implemented widely in the fields of marine biodiversity detection, marine search and rescue, and seabed mapping, providing an essential basis for human marine economic and military activities. With the rapid development of machine-learning-based technology in the acoustics field, these methods receive wide attention and display a potential impact on UATR problems. This paper reviews current UATR methods based on machine learning. We focus mostly, but not solely, on the recognition of target-radiated noise from passive sonar. First, we provide an overview of the underwater acoustic acquisition and recognition process and briefly introduce the classical acoustic signal feature extraction methods. In this paper, recognition methods for UATR are classified based on the machine learning algorithms used as UATR technologies using statistical learning methods, UATR methods based on deep learning models, and transfer learning and data augmentation technologies for UATR. Finally, the challenges of UATR based on the machine learning method are summarized and directions for UATR development in the future are put forward. Full article