Design and research on the fatigue detection system of ship bridge duty based on image processing

With the development of the marine economy, the number of ships is increasing day by day, and is developing towards large-scale, diversified and professional development, and marine accidents caused by driver fatigue have attracted more and more attention. In order to reduce marine traffic accidents caused by fatigue driving of ship drivers and ensure the safety of life and property at sea, it is very necessary and important to study effective methods to detect the fatigue state of ship drivers in real time. This article mainly studies the early warning of ship fatigue driving. In view of the difficulties of the ship fatigue driving detection technology, reasonable performance indicators of the ship anti-fatigue driving image processing and early warning system are proposed; according to the system performance indicators, the HOG+SVM method is determined to automatically track the human face, and the human eye detection and tracking method is designed. Improved the method of eyelid closure to determine fatigue. In order to determine the eye opening and closing state or blinking frequency. The PERCLOS method is used to determine whether the driver is tired, and a warning is given when the ship’s watch driver is tired. The system has the characteristics of non-contact, real-time, etc. and complies with the relevant technical standards of the International Maritime Organization (IMO) on the ship bridge fatigue warning system (BNWAS).

A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals

Traffic accidents are easily caused by tired driving. If the fatigue state of the driver can be identified in time and a corresponding early warning can be provided, then the occurrence of traffic accidents could be avoided to a large extent. At present, the recognition of fatigue driving states is mostly based on recognition accuracy. Fatigue state is currently recognized by combining different features, such as facial expressions, electroencephalogram (EEG) signals, yawning, and the percentage of eyelid closure over the pupil over time (PERCLoS). The combination of these features increases the recognition time and lacks real-time performance. In addition, some features will increase error in the recognition result, such as yawning frequently with the onset of a cold or frequent blinking with dry eyes. On the premise of ensuring the recognition accuracy and improving the realistic feasibility and real-time recognition performance of fatigue driving states, a fast support vector machine (FSVM) algorithm based on EEGs and electrooculograms (EOGs) is proposed to recognize fatigue driving states. First, the collected EEG and EOG modal data are preprocessed. Second, multiple features are extracted from the preprocessed EEGs and EOGs. Finally, FSVM is used to classify and recognize the data features to obtain the recognition result of the fatigue state. Based on the recognition results, this paper designs a fatigue driving early warning system based on Internet of Things (IoT) technology. When the driver shows symptoms of fatigue, the system not only sends a warning signal to the driver but also informs other nearby vehicles using this system through IoT technology and manages the operation background.

Heterogeneous Signal Fusion Method in Driving Fatigue Detection Signals

Driving fatigue is a physiological phenomenon that often occurs during driving. When the driver enters a fatigue state, they will become distracted and unresponsive, which can easily lead to traffic accidents. The driving fatigue detection method based on a single information source has poor stability in a specific driving environment and has great limitations. This work helps with being able to judge the fatigue state of the driver more comprehensively and achieving a higher accuracy rate of driving fatigue detection. This work mainly introduces research into different signal fusion methods to detect fatigue drive. This work will take the normal driver’s breathing signal, eye signals, and steering wheel signal as research objects and collect and isolate the characteristics of the fatigue detection signal. Research was then conducted on different signal fusion methods for the detected depth of breath. Change of steering angle, eyelid closure, and blinking marks and the fatigue driving experiment was designed to evaluate the results of different data fusion methods. Experimental results show that the detection accuracy of the heterogeneous signal fusion method in fatigue detection is as high as 80%.

Driver Fatigue Detection Based On Facial Feature Analysis

Fatigue driving is one of the main causes of traffic accidents. In recent years, considerable attention has been paid to fatigue detection systems, which is an important solution for preventing fatigue driving. In order to prevent and reduce fatigue driving, a driver fatigue detection system based on computer vision is proposed. In this system, an improved face detection method is used to detect the driver’s face from the image obtained by a charge coupled device (CCD) camera. Then, the feature points of the eyes and mouth are located by an ensemble of regression trees. Next, fatigue characteristic parameters are calculated by the improved percentage of eyelid closure over the pupil over time algorithm. Finally, the state of drivers is evaluated by using a fuzzy neural network. The system can effectively monitor and remind the state of drivers so as to significantly avoid or decrease the occurrence of traffic accidents. The experimental results show that the system is of wonderful real-time performance and accurate recognition rate, so it meets the requirements of practicality in driver fatigue detection greatly.

Effects of Individual Differences on Measurements’ Drowsiness-Detection Performance

Individual differences (IDs) may reduce the detection-accuracy of drowsiness-driving by influencing measurements’ drowsiness-detection performance (MDDP). The purpose of this paper is to propose a model that can quantify the effects of IDs on MDDP and find measurements with less impact by IDs to build drowsiness-detection models. Through field experiments, drivers’ naturalistic driving data and subjective-drowsiness levels were collected, and drowsiness-related measurements were calculated using the double-layer sliding time window. In the model, MDDP was represented by |Z-statistics| of the Wilcoxon-test. First, the individual driver’s measurements were analysed by Wilcoxon-test. Next, drivers were combined in pairs, measurements of paired-driver combinations were analysed by Wilcoxon-test, and measurement’s IDs of paired-driver combinations were calculated. Finally, linear regression was used to fit the measurements’ IDs and changes of MDDP that equalled the individual driver’s |Z-statistics| minus the paired-driver combination’s |Z-statistics|, and the slope’s absolute value (|k|) indicated the effects of ID on the MDDP. As a result, |k| of the mean of the percentage of eyelid closure (MPECL) is the lowest (4.95), which illustrates MPECL is the least affected by IDs. The results contribute to the measurement selection of drowsiness-detection models considering IDs.

Neuroprotective Effect of Brimonidine against Facial Nerve Crush Injury in Rats via Suppressing GFAP/PAF Activation and Neuroinflammation

<b><i>Objective:</i></b> This study aimed to investigate the potential neuroprotective action of brimonidine against facial nerve crush injury in rats and the possible underlying mechanisms. <b><i>Methods:</i></b> Sixty Wistar adult rats were randomly and equally divided into 3 groups: 40 rats underwent unilateral facial nerve crush injury and were administered with either saline (intraperitoneal, <i>n</i> = 20) or brimonidine 1 mg/kg/day (intraperitoneal, <i>n</i> = 20) for 5 consecutive days. Functional and electromyographic recovery was recorded postoperatively. The facial nucleus of 5 mice in each group was analyzed for mRNA expression levels of GFAP, PAF, NT-4, P75^NTR, NF-κB, TNF-α, IL-6, and α₂-ARs by qRT-PCR. <b><i>Results:</i></b> Brimonidine promoted the recovery of vibrissae movement, eyelid closure, and electrophysiological function in a rat model of nerve crush injury. Hematoxylin and eosin staining and electron microscopy showed significant recovery of Schwann cells and axons in the brimonidine group. Brimonidine attenuated the crush-induced upregulation in GFAP and PAF mRNA (<i>p</i> &#x3c; 0.05), as well as enhanced the mRNA levels of NT-4 and P75^NTR (<i>p</i> &#x3c; 0.05), while decreased the expression of NF-κB, TNF-α and IL-6 (<i>p</i> &#x3c; 0.05). <b><i>Conclusions:</i></b> Brimonidine could promote the recovery of facial nerve crush injury in rats via suppressing of GFAP/PAF activation and neuroinflammation and increasing neurotrophic factors. Brimonidine may be apromising candidate agent for the treatment of facial nerve injury.