Deep learning-based ship driver fatigue detection method and system

A driver fatigue and deep learning technology, applied in biological models, character and pattern recognition, biological neural network models, etc., can solve problems such as poor fatigue recognition, weak algorithm robustness, and single application scenarios. Achieve the effect of improving accuracy and speed, meeting real-time requirements, and improving recognition rate

Pending Publication Date: 2021-07-23
DALIAN MARITIME UNIVERSITY +1
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AI Technical Summary

Problems solved by technology

Advantages: fast speed, better recognition effect under good lighting conditions; Disadvantages: unable to adapt to complex lighting conditions, single application scene
Advantages: The speed is also faster, and the robustness of the model is enhanced; Disadvantages: It is impossible to distinguish the degree of eye openi

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  • Deep learning-based ship driver fatigue detection method and system
  • Deep learning-based ship driver fatigue detection method and system
  • Deep learning-based ship driver fatigue detection method and system

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Embodiment Construction

[0054] In order to make the object, technical solution and advantages of the present invention clearer, the embodiments of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0055] like figure 1 Shown is a kind of ship driver's fatigue detection method based on deep learning disclosed by the present invention, comprising the following steps:

[0056] Step 1, obtain the current video frame image;

[0057] Step 2, build improved Retinaface face detection network, and extract the face position information and left eye, right eye, nose tip, left corner of mouth and right corner of mouth 5 of described current video frame image by described improved Retinaface face detection network The location information of the key points of the face;

[0058] Step 3, using the position information of the 5 key points of the human face to crop the eyes and mouth area pictures, and identify the opening and closing states of the eyes ...

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Abstract

The invention discloses a deep learning-based ship driver fatigue detection method and system. The method comprises the following steps: acquiring a current video frame image; carrying out face detection by an improved Retinaface face detection network, and marking five face key points of the left eye, the right eye, the nose tip, the left mouth corner and the right mouth corner at the same time; cutting adaptively eyes and mouth pictures according to the positions of the five face key points, and identifying the opening and closing states of the eyes and the mouth by an improved ShuffleNet v2 convolutional neural network; calculating PERCLOS parameters of the eyes and the mouth; and comprehensively judging whether a driver is fatigued or not through a random forest model by fusing the PERCLOS parameters of the eyes and the mouth. Face detection and key point positioning can be rapidly realized, manual picture information extraction is not needed, the opening and closing states of the eyes and the mouth can be automatically identified, and eye and mouth feature parameters are fused based on the idea of multi-feature fusion, so that the fatigue state of the ship driver can be rapidly and accurately detected.

Description

technical field [0001] The invention belongs to the technical field of shipping, in particular to a method and system for detecting ship driver fatigue based on deep learning. Background technique [0002] my country is a big ocean country, and the shipping industry is developing very rapidly. In my country, more than 90% of foreign trade materials are shipped by sea. With the rapid development of modern technology and shipbuilding technology, the overall strength of my country's shipping industry has been significantly improved. However, there are still many safety problems in the process of rapid development of the shipping industry, and safety accidents occur from time to time, which pose a great threat to crew members, ships and the marine environment. According to statistics, 80% of marine safety accidents are related to human factors, among which marine accidents caused by fatigue driving account for a considerable proportion. In this context, it is particularly imp...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/00
CPCG06N3/006G06V40/165G06V40/171G06V20/597G06N3/045G06F18/214
Inventor 尹勇王鹏
Owner DALIAN MARITIME UNIVERSITY
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