AB-DLM: An Improved Deep Learning Model Based on Attention Mechanism and BiFPN for Driver Distraction Behavior Detection

MRI image classification of brain tumors is critical for accurate and early diagnosis. New developments in deep learning have revealed that inserting attention mechanisms into convolutional neural networks can greatly improve classification performance. This work assesses the effectiveness of Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM) integrated with the ResNet50 model for brain tumor classification. The SE module recalibrates channel-wise feature responses, while CBAM enhances feature representation by combining channel and spatial attention. The SE and CBAM are combined with ResNet50, greatly increasing classification accuracy, precision, and recall when compared to the basic model, according to experimental results on MRI datasets. The suggested model employs attention mechanisms to focus valuable information selectively and suppress irrelevant information. The experiments on publicly available brain tumor datasets display great improvements over basic CNN models, with precision, recall, accuracy, as well as F1 score at 0.9914, 0.9903, 0.9945, and 0.9908, respectively. From these results, the importance of attention mechanisms in deep learning models for medical imaging is highlighted, which suggests that SE and CBAM modules can be available as more dependable and effective instruments for brain tumor classification in clinical settings. Future studies should investigate transformer-based and hybrid attention techniques to enhance automated brain tumor categorization.

Driver distraction has become an important and growing safety concern as the use of in-vehicle information systems (IVISs), such as cell phones and navigation systems, continues to increase. One approach to allowing people to benefit from IVISs without compromising safety is to create adaptive IVISs that adjust their functions according to driver and roadway state. A critical element of adaptive IVISs involves monitoring driver distraction in real time; with such a monitoring function it is possible to mitigate that distraction. This study applied Bayesian networks (BNs), a data mining method, to develop a real-time approach to detecting cognitive distraction using drivers’ eye movements and driving performance. Data were collected in a simulator experiment involving 10 participants who interacted with an IVIS system while driving. BN models were trained and tested to investigate the influence of three model characteristics on distraction detection: time history of driver behavior, inclusion of hidden nodes in the model structure, and how data are summarized and the length of training sequences. Results showed that BNs could identify driver distraction reliably with an average accuracy of 80.1%. Dynamic BNs (DBNs) that consider time dependencies of driver behavior produced more sensitive models than static BNs (SBNs). Longer training sequences improved DBN model performance. Blink frequency and fixation measures were particularly indicative of distraction. These results demonstrate that BNs, especially DBNs, can detect driver cognitive distraction by extracting information from complex behavioral data. Potential applications include the design of adaptive in-vehicle systems and the evaluation of driver distraction.

Driver drowsiness and distraction are two main reasons for traffic accidents and the related financial losses. Therefore, researchers have been working for more than a decade on designing driver inattention monitoring systems. As a result, several detection techniques for the detection of both drowsiness and distraction have been proposed in the literature. Some of these techniques were successfully adopted and implemented by the leading car companies. This paper discusses and provides a comprehensive insight into the well-established techniques for driver inattention monitoring and introduces the use of most recent and futuristic solutions exploiting mobile technologies such as smartphones and wearable devices. Then, a proposal is made for the active of such systems into car-to-car communication to support vehicular ad hoc network's (VANET's) primary aim of safe driving. We call this approach the dissemination of driver behavior via C2C communication. Throughout this paper, the most remarkable studies of the last five years were examined thoroughly in order to reveal the recent driver monitoring techniques and demonstrate the basic pros and cons. In addition, the studies were categorized into two groups: driver drowsiness and distraction. Then, research on the driver drowsiness was further divided into two main subgroups based on the exploitation of either visual features or nonvisual features. A comprehensive compilation, including used features, classification methods, accuracy rates, system parameters, and environmental details, was represented as tables to highlight the (dis)advantages and/or limitations of the aforementioned categories. A similar approach was also taken for the methods used for the detection of driver distraction.

Pine wilt disease (PWD) is a highly destructive worldwide forest quarantine disease that has the potential to destroy entire pine forests in a relatively brief period, resulting in significant economic losses and environmental damage. Manual monitoring, biochemical detection and satellite remote sensing are frequently inadequate for the timely detection and control of pine wilt disease. This paper presents a fusion model, which integrates the Mamba model and the attention mechanism, for deployment on unmanned aerial vehicles (UAVs) to detect infected pine trees. The experimental dataset presented in this paper comprises images of pine trees captured by UAVs in mixed forests. The images were gathered primarily during the spring of 2023, spanning the months of February to May. The images were subjected to a preprocessing phase, during which they were transformed into the research dataset. The fusion model comprised three principal components. The initial component is the Mamba backbone network with State Space Model (SSM) at its core, which is capable of extracting pine wilt features with a high degree of efficacy. The second component is the attention network, which enables our fusion model to center on PWD features with greater efficacy. The optimal configuration was determined through an evaluation of various attention mechanism modules, including four attention modules. The third component, Path Aggregation Feature Pyramid Network (PAFPN), facilitates the fusion and refinement of data at varying scales, thereby enhancing the model’s capacity to detect multi-scale objects. Furthermore, the convolutional layers within the model have been replaced with depth separable convolutional layers (DSconv), which has the additional benefit of reducing the number of model parameters and improving the model’s detection speed. The final fusion model was validated on a test set, achieving an accuracy of 90.0%, a recall of 81.8%, a map of 86.5%, a parameter counts of 5.9 Mega, and a detection speed of 40.16 FPS. In comparison to Yolov8, the accuracy is enhanced by 7.1%, the recall by 5.4%, and the map by 3.1%. These outcomes demonstrate that our fusion model is appropriate for implementation on edge devices, such as UAVs, and is capable of effective detection of PWD.

In order to solve the problem of excessive model parameters and low real‐time performance in driver distraction driving detection tasks, this work proposes a detection model based on cascaded convolutional network and attention mechanism. The model adopts a two‐stage architecture. In the first stage, the pre‐trained MobileNet is used as the backbone network for basic feature extraction to achieve efficient image feature extraction and significantly reduce the computational complexity. In the second stage, the basic features extracted in the first stage are enhanced by combining the Cascaded ResNet structure with the spatial attention mechanism, so as to improve the capture ability of key features. Finally, the features extracted in the two stages are fused to complete the driver's distraction behavior recognition. The experimental results on the public datasets American University in Cairo (AUC) Distracted Driver and StateFarm Distracted Driver (SFD) show that the proposed model achieves the recognition accuracy of 95.72% and 99.87%, respectively, which is significantly better than the existing mainstream methods while maintaining a low number of parameters. The model has low parameter quantity, high detection accuracy and high real‐time performance.

Driver distraction is one of the important factors leading to traffic accidents. With the development of mobile infotainment and the overestimation of immature autonomous driving technology, this phenomenon has become more and more serious. However, most existing distraction detection algorithms can not achieve satisfactory performance due to the complex in-cabin light condition and limited computing resource of edge devices. To this end, we introduce a light weight and flexible event-based system to monitor driver state. Compared with frame-based camera, the event camera responds to pixel wise light intensity changes asynchronously and has several promising advantages, including high dynamic range, high temporal resolution, low latency and low data redundant, which makes it suitable for the mobile terminal applications. The system first denoises the events stream and encode it into a sequence of 3D tensors. Then, the voxel features at different time steps are extracted using efficient net and fed into LSTM to establish temporal model, based on which, the driver distraction is detected. In addition, we extend the proposed architecture to recognise driver action and adopt transfer learning strategy to improve the detection performance. Extensive experiments are conducted on both simulated dataset (transform from Drive&Act) and real event dataset (collected by ourselves). The experimental results shows the advantages of the system on accuracy and efficient for driver state monitoring.

At present, the ship detection of optical remote sensing images based on deep learning has made great progress. However, due to the different use scenarios and specific tasks, how to select an appropriate algorithm according to the characteristics of the target and the target priority, so that achieve the detection goal while consider the detection accuracy and speed, still requires relevant research. In this paper, ship detection methods for optical remote sensing images are studied based on deep learning. First, to meet the needs of ship detection research, according to the characteristics of target size and type, datasets of medium and large ships and small target ships are made, and model training and testing are conducted based on Faster R-CNN, YOLOv4, and SSD algorithms respectively. The actual detection performance of the three algorithms under different ship sizes is obtained. The results show that for medium and large targets, Faster R-CNN has the highest precision, the next is YOLOv4, and SSD is the lowest. The detection speed is that SSD is the fastest, the next is YOLOv4, Faster R-CNN is the slowest. For small target ship detection, YOLOv4 has the best detection accuracy and SSD has the fastest detection speed. Faster R-CNN is not as accurate and fast as the other two algorithms. In addition, for different type ships, the detection results of different algorithms also have some differences. In practical applications, different methods should be used to achieve detection by comprehensively considering such factors as target size, target priority, detection accuracy and speed requirements.

Driver distraction is considered a main cause of road accidents, every year, thousands of people obtain serious injuries, and most of them lose their lives. In addition, a continuous increase can be found in road accidents due to driver’s distractions, such as talking, drinking, and using electronic devices, among others. Similarly, several researchers have developed different traditional deep learning techniques for the efficient detection of driver activity. However, the current studies need further improvement due to the higher number of false predictions in real time. To cope with these issues, it is significant to develop an effective technique which detects driver’s behavior in real time to prevent human lives and their property from being damaged. In this work, we develop a convolutional neural network (CNN)-based technique with the integration of a channel attention (CA) mechanism for efficient and effective detection of driver behavior. Moreover, we compared the proposed model with solo and integration flavors of various backbone models and CA such as VGG16, VGG16+CA, ResNet50, ResNet50+CA, Xception, Xception+CA, InceptionV3, InceptionV3+CA, and EfficientNetB0. Additionally, the proposed model obtained optimal performance in terms of evaluation metrics, for instance, accuracy, precision, recall, and F1-score using two well-known datasets such as AUC Distracted Driver (AUCD2) and State Farm Distracted Driver Detection (SFD3). The proposed model achieved 99.58% result in terms of accuracy using SFD3 while 98.97% accuracy on AUCD2 datasets.

BackgroundAntimicrobial peptides (AMPs) are short peptides with diverse biological activities and playing a crucial role in various biological processes. Due to the widespread misuse of traditional antibiotics and the increasing resistance of microorganisms to these drugs, AMPs have emerged as a promising alternative. Consequently, the identification of AMPs has garnered significant research interest. Numerous computational methods based on machine learning algorithms have been developed to facilitate AMP recognition. However, some existing AMPs recognition models only focus on binary classification tasks or only identify the functional activity of a limited number of AMPs categories in multi-class classification tasks. To address this limitation, this study proposes a two-stage AMPs recognition model, iAMP-SeE.MethodsThe iAMP-SeE model extracts features from protein sequences using ESM2, employs a Convolutional Neural Network (CNN) module to capture local patterns from ESM features and utilizes a Bidirectional Long Short-Term Memory (BiLSTM) network to capture long-term dependencies. Furthermore, it incorporates Squeeze-and-Excitation (SE) and Efficient Channel Attention (ECA) mechanisms, which focus on global and local channel relationships, respectively. These two attention mechanisms are complementary, as they enhance features across various dimensions and granularities while simultaneously suppressing irrelevant or redundant features, thereby boosting the model’s performance. Additionally, to address the issue of imbalanced datasets, the Synthetic Minority Over-sampling Technique (SMOTE) is incorporated into the multi-classification task. This method balances the number of AMP categories and ensures that minority classes are not overlooked during model training.ResultsEvaluation across a range of classification thresholds demonstrated the stability of the model’s performance metrics in both binary and multi-class tasks. Furthermore, comparative experiments with existing AMP recognition models confirmed the superior performance of iAMP-SeE.ConclusionsRigorous experimental comparisons and ablation studies demonstrate the effectiveness of iAMP-SeE for both binary and multi-class AMP classification tasks. The source code is publicly available at: https://github.com/cqw0715/iAMP-SeE.git.

During the manufacturing process of printed circuit boards (PCBs), quality defects can occur, which can affect the performance and reliability of PCBs. Existing deep learning-based PCB defect detection methods are difficult to simultaneously achieve the goals of high detection accuracy, fast detection speed, and small number of parameters. Therefore, this paper proposes a PCB defect detection algorithm based on CDI-YOLO. Firstly, the coordinate attention mechanism (CA) is introduced to improve the backbone and neck network of YOLOv7-tiny, enhance the feature extraction capability of the model, and thus improve the accuracy of model detection. Secondly, DSConv is used to replace part of the common convolution in YOLOv7-tiny to achieve lower computing costs and faster detection speed. Finally, Inner-CIoU is used as the bounding box regression loss function of CDI-YOLO to speed up the bounding box regression process. The experimental results show that the method achieves 98.3% mAP on the PCB defect dataset, the detection speed is 128 frames per second (FPS), the parameters is 5.8 M, and the giga floating-point operations per second (GFLOPs) is 12.6 G. Compared with the existing methods, the comprehensive performance of this method has advantages.

Traffic light detection and recognition are crucial for enhancing the security of unmanned systems. This study proposes a YOLOv5-based traffic light-detection algorithm to tackle the challenges posed by small targets and complex urban backgrounds. Initially, the Mosaic-9 method is employed to enhance the training dataset, thereby boosting the network’s ability to generalize and adapt to real-world scenarios. Furthermore, the Squeeze-and-Excitation (SE) attention mechanism is incorporated to improve the network. Moreover, the YOLOv5 algorithm’s loss function is optimized by substituting it with Efficient Intersection over Union loss (EIoU_loss), which addresses issues like missed detection and false alarms. Experimental results demonstrate that the model trained with this enhanced network achieves an mAP (mean average precision) of 99.4% on a custom dataset, which is 6.3% higher than that of the original YOLOv5, while maintaining a detection speed of 74 f/s. Therefore, this algorithm offers higher detection accuracy and effectively meets real-time operational requirements. The proposed method has a strong application potential, and can be widely used in the field of automatic driving, assisted driving, etc. Its application is not only of great significance in improving the accuracy and speed of traffic sign detection, but also can provide technical support for the development of intelligent transportation systems.

In the future, vehicles will be equipped with increasingly advanced interactive intelligent electronic devices, which will induce drivers to conduct secondary tasks, thereby leading to distractions. Therefore, the detection and early warning of driver distraction are essential for improving driving safety and pose an important challenge in intelligent transportation systems. Previous studies used traditional machine learning and deep learning transfer models, which have the disadvantages of complicated and time-consuming manual feature engineering, strong subjectivity, and weak generalization performance. In this paper, we propose a fine-grained detection method for driver distraction based on neural architecture search. First, we design an automatic construction algorithm for deep convolutional neural networks based on neural architecture search, which automatically searches for the optimal deep convolutional neural network architecture without human involvement. In addition, we fuse driver-related multisource perception information, use an automatically constructed deep convolutional neural network to extract high-dimensional mapping features, and implement fine-grained detection of various types of driver distraction states. The results on a large-scale multimodal driver distraction dataset demonstrate that our method can efficiently search an optimal deep convolutional neural network, which can quickly converge, and can accurately detect the considered types of driver distraction states, the average detection accuracy reaches 99.7796%; moreover, it has satisfactory robustness.

Bountiful reasons are there for crop up of road accidents. Predominantly most of the road accidents are turn out by the humans. In compare with other sources of road accidents, distracted driving is the foremost one. Driver gets distracted ascribed to many factors such as texting using phone, seeing outside of the car. The regime can grasp the road accidents by enforcing the traffic regulations moreover the regime seeks the assistance from the technology to reduce the car accidents due to driver distraction. There are numerous research undergoing to reduce the chance of the driver to get distracted by monitoring the physical activities of the driver while driving the four wheeler. Automatic detection of driver distraction can support to develop an apprise system by observing the activities of the driver that provide better result and evading the car accidents. Deep learning is a leading technology in automated industries to detect the traffic light signs and pedestrians. The novel ResNext 101 is the proposed work and inherited from its parent model called Residual Network (ResNet) to train and test the model. The “Next” in the word ResNext represents next dimension of ResNet called as “cardinality”. In this paper, with the help of Convolutional Neural Network (CNN), the system detects and captures the distracted moments of the driver from proper driving gestures automatically in the form of 2D image. The test results of the model outperforms and proved its significance over the existing driver detection algorithms by bagging higher efficacy with 97.6% accuracy in classifying the live driver posture.

Detection of driver cognitive distraction is critical for active safety systems of road vehicles. Compared with visual distraction, cognitive distraction is more challenging for detection due to the lack of apparent exterior features. This paper presents a novel real-time detection algorithm for driver cognitive distraction by using support vector machine (SVM). Data are collected from 26 subjects, driving in typical urban and highway scenarios in a simulator. The chosen urban scenario is the stop-controlled intersection and the highway scenario is the speed-limited highway. Driver cognitive distraction while driving is induced by clock tasks which compete with the main driving tasks for visuospatial short working memory. For each subject, distracted driving instances and the equal number of non-distracted driving instances were collected (24 for urban scenario and 20 for highway scenario in total). Features concerning both driving performance and eye movement are used for training and validation. The proposed algorithm have correct rate of 93.0% and 98.5% for highway and urban scenarios respectively. Results also show that driver distraction can be recognized 6.5 s to 9.0 s after its happening, indicating good performance of the detection algorithm.

To address the current problems of the incomplete classification of mask-wearing detection data, small-target miss detection, and the insufficient feature extraction capabilities of lightweight networks dealing with complex faces, a lightweight method with an attention mechanism for detecting mask wearing is presented in this paper. This study incorporated an “incorrect_mask” category into the dataset to address incomplete classification. Additionally, the YOLOv4-tiny model was enhanced with a prediction feature layer and feature fusion execution, expanding the detection scale range and improving the performance on small targets. A CBAM attention module was then introduced into the feature enhancement network, which re-screened the feature information of the region of interest to retain important feature information and improve the feature extraction capabilities. Finally, a focal loss function and an improved mosaic data enhancement strategy were used to enhance the target classification performance. The experimental results of classifying three objects demonstrate that the lightweight model’s detection speed was not compromised while achieving a 2.08% increase in the average classification precision, which was only 0.69% lower than that of the YOLOv4 network. Therefore, this approach effectively improves the detection effect of the lightweight network for mask-wearing.