Underwater Rescue Target Detection Based on Acoustic Images

Abstract

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.

A Simulation Method for Underwater SPAD Depth Imaging Datasets

“…The information carried by underwater images can be used to objectively and efficiently display real deep-sea scenes, greatly facilitating the exploration of the underwater world. Images obtained through underwater optical imaging have a high resolution and carry a large amount of information, making them widely applicable in areas such as ocean energy exploration, underwater rescue, marine environmental monitoring, and maritime military operations [ 1 ].…”

A Pruning and Distillation Based Compression Method for Sonar Image Detection Models

“…Yan et al [20] improved the MobileNet-SSD benchmark network for underwater sonar target detection, greatly enhancing the network's inference speed. Hu et al [21] integrated ShuffleNetv2 into YOLOv5s to make the model more compact, improving computational speed on embedded devices. Qin et al [22] pruned the improved YOLOv7 using channel pruning methods, reducing model memory by 47.50% and increasing detection speed by nearly 2.5 times for forward-looking sonar target detection.…”