Driver fatigue has been attributed to traffic accidents; therefore, fatigue-related traffic accidents have a higher fatality rate and cause more damage to the surroundings compared with accidents where the drivers are alert. Recently, many automobile companies have installed driver assistance technologies in vehicles for driver assistance. Third party companies are also manufacturing fatigue detection devices; however, much research is still required for improvement. In the field of driver fatigue detection, continuous research is being performed and several articles propose promising results in constrained environments, still much progress is required. This paper presents state-of-the-art review of recent advancement in the field of driver fatigue detection. Methods are categorized into five groups, i.e., subjective reporting, driver biological features, driver physical features, vehicular features while driving, and hybrid features depending on the features used for driver fatigue detection. Various approaches have been compared for fatigue detection, and areas open for improvements are deduced.

Driver Fatigue Detection Systems: A Review

In a telemedicine diagnosis system, the emergence of 3D imaging enables doctors to make clearer judgments, and its accuracy also directly affects doctors’ diagnosis of the disease. In order to ensure the safe transmission and storage of medical data, a 3D medical watermarking algorithm based on wavelet transform is proposed in this paper. The proposed algorithm employs the principal component analysis (PCA) transform to reduce the data dimension, which can minimize the error between the extracted components and the original data in the mean square sense. Especially, this algorithm helps to create a bacterial foraging model based on particle swarm optimization (BF-PSO), by which the optimal wavelet coefficient is found for embedding and is used as the absolute feature of watermark embedding, thereby achieving the optimal balance between embedding capacity and imperceptibility. A series of experimental results from MATLAB software based on the standard MRI brain volume dataset demonstrate that the proposed algorithm has strong robustness and make the 3D model have small deformation after embedding the watermark. 

https://file.techscience.com/ueditor/files/csse/TSP_CSSE-41-3/TSP_CSSE_22305/TSP_CSSE_22305.pdf

A Robust 3-D Medical Watermarking Based on Wavelet Transform for Data Protection

The leakage of medical audio data in telemedicine seriously violates the privacy of patients. In order to avoid the leakage of patient information in telemedicine, a two-stage reversible robust audio watermarking algorithm is proposed to protect medical audio data. The scheme decomposes the medical audio into two independent embedding domains, embeds the robust watermark and the reversible watermark into the two domains respectively. In order to ensure the audio quality, the Hurst exponent is used to find a suitable position for watermark embedding. Due to the independence of the two embedding domains, the embedding of the second-stage reversible watermark will not affect the first-stage watermark, so the robustness of the first-stage watermark can be well maintained. In the second stage, the correlation between the sampling points in the medical audio is used to modify the hidden bits of the histogram to reduce the modification of the medical audio and reduce the distortion caused by reversible embedding. Simulation experiments show that this scheme has strong robustness against signal processing operations such as MP3 compression of 48 db, additive white Gaussian noise (AWGN) of 20 db, low-pass filtering, resampling, re-quantization and other attacks, and has good imperceptibility. 

https://www.techscience.com/cmc/v71n2/45815/pdf

Robust Reversible Audio Watermarking Scheme for Telemedicine and Privacy Protection

The proper planning of rest periods in response to the availability of parking spaces at rest areas is an important issue for haulage companies as well as traffic and road administrations We present a case study of how You Only Look Once (YOLO)v5 can be implemented to detect heavy goods vehicles at rest areas during winter to allow for the real-time prediction of parking spot occupancy Snowy conditions and the polar night in winter typically pose some challenges for image recognition, hence we use thermal network cameras As these images typically have a high number of overlaps and cut-offs of vehicles, we applied transfer learning to YOLOv5 to investigate whether the front cabin and the rear are suitable features for heavy goods vehicle recognition Our results show that the trained algorithm can detect the front cabin of heavy goods vehicles with high confidence, while detecting the rear seems more difficult, especially when located far away from the camera In conclusion, we firstly show an improvement in detecting heavy goods vehicles using their front and rear instead of the whole vehicle, when winter conditions result in challenging images with a high number of overlaps and cut-offs, and secondly, we show thermal network imaging to be promising in vehicle detection

Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5

This paper proposes a soft tissue grasping deformation model, where BP neural network optimized by the genetic algorithm is used to realize the real-time and accurate interaction of soft tissue grasping during virtual surgery. In the model, the soft tissue epidermis is divided into meshes, and the meshes generate displacements under the action of tension. The relationship between the tension and displacement of the mesh is determined by the proposed cylindrical spiral spring model. The optimized BP neural network is trained based on the sample data of the mesh point and vertical tension, so as to obtain the force and displacement of any mesh point on the soft tissue epidermis. The virtual experiment platform is built using a PHANTOM OMNI haptic hand controller and the 3D Max software, by which the simulation experiment of grasping the human abdomen is realized. The experimental results show that the proposed model has good visual interaction and real-time force feedback, which can meet the requirements of deformation simulation for soft tissue grasping in virtual surgery. 

Deformation Expression of Soft Tissue Based on BP Neural Network

https://www.techscience.com/csse/v41n3/45555/pdf

Vehicle type classification (VTC) plays an important role in today’s intelligent transportation. Previous VTC systems usually run on a monitoring center’s host machine due to the models’ complexity, which consume lots of computing resources and have poor real-time performance. If these systems are deployed to embedded terminals by making the model lightweight while ensuring accuracy, then the problem can be addressed. To this end, we propose a fine-grained VTC method using lightweight convolutional neural network with feature optimization and joint learning strategy. Firstly, a lightweight convolutional network with feature optimization (LWCNN-FO) is designed. We use depthwise separable convolution to reduce network parameters. Besides, the SENet module is added to obtain the important degree of each feature channel automatically through the sample-based self-learning, which can improve recognition accuracy with less network parameters growth. In addition, considering both between-class similarity and intra-class variance, this paper adopts the joint learning strategy combining softmax loss and contrastive-center loss to class vehicle types, thereby improving model’s fine-grained classification ability. We also build a dataset, called Car-159, consisting of 7998 pictures for 159 vehicle types, to evaluate our method. Compared with the state-of-the-art methods, experimental results show that our method can effectively decrease model’s complexity while maintaining accuracy.

Fine-grained vehicle type classification using lightweight convolutional neural network with feature optimization and joint learning strategy

https://www.techscience.com/iasc/v32n2/45571/pdf

https://www.techscience.com/cmc/v69n3/44189/pdf

A Multi-Feature Learning Model with Enhanced Local Attention for Vehicle Re-Identification

The key to preventing the COVID-19 is to diagnose patients quickly and accurately. Studies have shown that using Convolutional Neural Networks (CNN) to analyze chest Computed Tomography (CT) images is helpful for timely COVID-19 diagnosis. However, personal privacy issues, public chest CT data sets are relatively few, which has limited CNN's application to COVID-19 diagnosis. Also, many CNNs have complex structures and massive parameters. Even if equipped with the dedicated Graphics Processing Unit (GPU) for acceleration, it still takes a long time, which is not conductive to widespread application. To solve above problems, this paper proposes a lightweight CNN classification model based on transfer learning. Use the lightweight CNN MobileNetV2 as the backbone of the model to solve the shortage of hardware resources and computing power. In order to alleviate the problem of model overfitting caused by insufficient data set, transfer learning is used to train the model. The study first exploits the weight parameters trained on the ImageNet database to initialize the MobileNetV2 network, and then retrain the model based on the CT image data set provided by Kaggle. Experimental results on a computer equipped only with the Central Processing Unit (CPU) show that it consumes only 1.06 s on average to diagnose a chest CT image. Compared to other lightweight models, the proposed model has a higher classification accuracy and reliability while having a lightweight architecture and few parameters, which can be easily applied to computers without GPU acceleration. Code:github.com/ZhouJie-520/paper-codes. © 2022 Tech Science Press. All rights reserved.

https://www.techscience.com/cmc/v72n1/46900/pdf

Xiaorui Zhang

Papers

A Robust 3-D Medical Watermarking Based on Wavelet Transform for Data Protection

Fine-grained vehicle type classification using lightweight convolutional neural network with feature optimization and joint learning strategy

Deformation Expression of Soft Tissue Based on BP Neural Network

A Multi-Feature Learning Model with Enhanced Local Attention for Vehicle Re-Identification

A Lightweight CNN Based on Transfer Learning for COVID-19 Diagnosis