Many of the major casualties caused by ship sailing accidents have proved that life jackets play a vital role in safeguarding the lives of people on board. In this paper, the K-means++ algorithm is used to optimize the target prior frame, and the CBAM attention mechanism is added to the YOLOv7 network to enhance the effective features and weaken the ineffective features in the feature extraction process, to enhance the localization accuracy and detection ability. The BiFPN network is used to enhance the information acquisition degree of the target, and finally, a comparison experiment is conducted with the YOLOv7 model before improvement. The experimental results show that the accuracy of the YOLOv7-CB model after training on the lifejacket dataset reaches 92.3%, the detection rate is 6.4% higher than that of the pre-improvement period, and the detection rate is 8.5% higher, which is good for the lifejacket wearing detection of shipboard personnel in different environments.
In order to further respond to the national policy and improve the monitoring capability of the sealing status of inland bulk carriers on the Yangtze River, this paper proposes the C-YOLOv5 model on the basis of the YOLOv5 algorithm. The elkan K-Means clustering algorithm is introduced to optimize the target candidate frame so that it can adapt to the detection environment of small targets while ensuring the recall and accuracy of recognition. In order to improve the focus on the detected targets, the SE focus mechanism module is introduced in the head of the model. In addition, some of the ordinary convolutions in the structure are replaced with depth-separable convolutions to further improve the detection speed. The experimental results show that the detection accuracy and speed are improved by using the C-YOLOv5 model, with an accuracy as high as 91.1% and a speed as high as 66.5 f/s. The detection accuracy and retrieval rate of the C-YOLOv5 algorithm both reach 90%. The C-YOLOv5 algorithm's ability to recognize the target state is also significantly improved under poor line-of-sight and dark light test conditions.
To solve the problems of low recognition accuracy and slow detection of crew fatigue driving behavior in the cockpit of ships during the process of sailing in and out of the port, the SSD model was studied. By replacing its backbone network and improving the prior frame generation mechanism, the MV-SSD model is proposed. Replace the backbone network VGG16 in the original SSD model with MobileNetV3, reducing the network parameters of the backbone network. Using the K-means algorithm to cluster the real detection boxes in the face area dataset, the prior box allocation mechanism of the SSD model was redesigned, reducing the number of prior box generation by nearly half, and the ERT algorithm in the Dlib library is combined to locate the face key points, and finally, the PERCLOS criterion is used to determine whether the driver is fatigued. Experimental results show that the average accuracy (mAP value) of the MV-SSD model is 7.15% higher than that of the original SSD model, and the detection speed (FPS value) is increased by 98frames/s, which is more suitable for the detection of the crew face area, and the average accuracy of the constructed fatigue detection algorithm for fatigue features is more than 94%.
This paper firstly identified the risk factors of the port infrastructure investment environment under the background of PPP (public-private partnership), and constructed the first-level index risk evaluation index system. The weight of the indicators was then determined using the Delphi method, and the indicators were graded according to questionnaires and generally accepted rules for ranking. Finally, taking the infrastructure project of Zhenhai Harbor District in Ningbo as the research object, the risk evaluation model of port infrastructure investment is established by the method of expert investigation and the fuzzy comprehensive evaluation. The results showed that the overall risk of the infrastructure project of Zhenhai Harbor District in Ningbo is moderate, and among all the first-class index risk factors, the operational risk and master/marine pilot’s risky behaviour are high risks, effective control should be given first; of all the secondary indicator risk factors, the division of rights and responsibilities between the two sides is the riskiest. Under the mode of public-private partnership, the risky factors in the process of port construction and operation should be monitored in real-time, and effective risk control measures should be taken.
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