Underwater Drowning People Detection Based On Bottleneck Transformer And Feature Pyramid Network

Chen, Haojie; Yuan, Hao; Qin, Hongde; Mu, Xiaokai

doi:10.1109/icus55513.2022.9986998

Cited by 1 publication

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several fully connected layers near the output layer enhance the ability to map high-level feature vectors to the semantic space of sample classes, and optical images with a similar shape as the same target class as sonar features. Chen et al [ 17 ] embedded a multi-head self-attention mechanism into the acoustic target detection network, and captured poor acoustic target features by establishing a global dependency to improve the accuracy of target detection. Li et al [ 18 ] proposed a transformer feature fusion network based on the transformer stack structure to promote information fusion.…”

Section: Introductionmentioning

confidence: 99%

Underwater Rescue Target Detection Based on Acoustic Images

Hu,

Liu

2024

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%