A case study of utilizing YOLOT based quantitative detection algorithm for marine benthos

Current object detection algorithms suffer from low accuracy and poor robustness when used to detect marine benthos due to the complex environment and low light levels on the seabed. To solve these problems, the YOLOT (You Only Look Once with Transformer) algorithm, a quantitative detection algorithm based on the improved YOLOv4, is proposed for marine benthos in this paper. To improve the feature extraction capability of the neural network, the transformer mechanism is introduced in the backbone feature extraction network and feature fusion part of YOLOv4, which enhances the adaptability of the algorithm to targets in complex undersea environments. On the one hand, the self-attention unit is embedded into CSPDarknet-53, which improves the feature extraction capability of the network. On the other hand, it is transformer-based feature fusion rules that are introduced to enhance the extraction of contextual semantic information in the feature pyramid network. In addition, prob-abilistic anchor assignment based on Gaussian distribution is introduced to network training. The experimental validation shows that compared with the original YOLOv4, the YOLOT algorithm improves the recognition precision from 75.35% to 84.44% on the marine benthic dataset. The improvement reflects that YOLOT is suitable for the quantitative detection of marine benthos.

Automated summary

This paper proposes the YOLOT algorithm, a quantitative detection algorithm based on the improved YOLOv4, for detecting marine benthos. By introducing the Transformer mechanism and probabilistic anchor assignment, the algorithm enhances the feature extraction capability and adaptability in complex environments. Experimental results show that YOLOT achieves higher recognition precision on the marine benthic dataset compared to the original YOLOv4.