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Multilabel Video Classification Model of Navigation Mark's Lights Based on Deep Learning.

Xu Han¹, Mingyang Pan¹, Haipeng Ge¹

¹Navigation College, Dalian Maritime University, Dalian 116026, China.

Computational Intelligence and Neuroscience

|November 22, 2021

Summary

This summary is machine-generated.

This study introduces NMLNet, an intelligent system for recognizing navigation mark lights at night. It achieves 99.23% accuracy in classifying 9 light types, improving maritime safety through advanced sensing.

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Area of Science:

Computer Vision
Maritime Technology
Artificial Intelligence

Background:

Navigation marks are identified by lights at night, posing challenges for interpretation.
Intelligent sensing of the navigation environment is a critical research area.
Current methods struggle to decipher the complex light signals from navigation aids.

Purpose of the Study:

To develop an intelligent system for recognizing navigation mark lights at night.
To investigate multilabel video classification methods for accurate light identification.
To compare different models for capturing light color and flashing phase characteristics.

Main Methods:

Utilized multilabel video classification, comparing binary relevance, label power set, and adapted algorithms.
Developed NMLNet, a binary relevance-based model with separate branches for color and flashing classification.
Employed an improved MobileNet-v2 with spatial attention for flashing classification and an LSTM for temporal dynamics.

Main Results:

NMLNet achieved 99.23% accuracy in classifying 9 types of navigation mark lights.
The model demonstrated the fastest computation speed with the fewest network parameters.
Optimized for mobile deployment, NMLNet achieved near Resnet-50 accuracy with high speed.

Conclusions:

NMLNet offers a highly accurate and efficient solution for intelligent navigation light recognition.
The system effectively captures both color and temporal flashing characteristics of navigation lights.
This advancement has significant potential for enhancing maritime safety and navigation efficiency.