Detecting bearing defects accurately and efficiently is critical for industrial safety and efficiency. This paper introduces Bearing-DETR, a deep learning model optimised using the Real-Time Detection Transformer (RT-DETR) architecture. Enhanced with Dysample Dynamic Upsampling, Efficient Model Optimization (EMO) with Meta-Mobile Blocks (MMB), and Deformable Large Kernel Attention (D-LKA), Bearing-DETR offers significant improvements in defect detection while maintaining a lightweight framework suitable for low-resource devices. Validated on a dataset from a chemical plant, Bearing-DETR outperformed the standard RT-DETR, achieving a mean average precision (mAP) of 94.3% at IoU = 0.5 and 57.5% at IoU = 0.5–0.95. It also reduced floating-point operations (FLOPs) to 8.2 G and parameters to 3.2 M, underscoring its enhanced efficiency and reduced computational demands. These results demonstrate the potential of Bearing-DETR to transform maintenance strategies and quality control across manufacturing environments, emphasising adaptability and impact on sustainability and operational costs.

The prediction of lane-change behavior is a challenging issue in intelligent and connected vehicles (ICVs), which can help vehicles predict in advance and change lanes safely. In this paper, a novel intelligent approach, which considering both the driving style-based lane-change environment and the driving trajectory-related parameters of the ICV and surrounding vehicles, is proposed to predict the lane-change behaviors for ICVs. By analyzing the characteristics of the lane-change behavior of the vehicle, a modified dataset for the prediction of lane-change behavior was established based on the Next-Generation Simulation (NGSIM) dataset, which is made up of real vehicle trajectories collected by camera. In the proposed approach, the hidden Markov model (HMM)-based model is designed to judge whether the current environment is suitable for lane change according to the driving environment parameters around the vehicle; then according to the driving state of the vehicle, a learning-based prediction-then-judgment model is proposed and designed to realize the prediction of the ICV’s lane-change behavior. Experiments are implemented by using the modified dataset. From the experimental results, the lane-change probability value on the target lane in the truth of the lane-change behavior calculated by the designed HMM-based model is basically above 0.5, indicating that the model can make a more accurate judgment on the surrounding lane-change environment. The proposed learning-based prediction-then-judgment model has an accuracy of 99.32% for the prediction of lane-change behavior, and the accuracy of the lane-change detection algorithm in the model is 99.56%. The experimental results show that the proposed approach has a good performance in the prediction of lane-change behavior, which could effectively assist ICVs to change lanes safely.

Cellular Vehicle-to-Everything (C-V2X) Mode 4, developed by 3GPP, supports vehicle-to-vehicle communication without a cellular base station via LTE sidelink. The messages transmitted in the Internet of Vehicles (IoV) include periodic single-hop broadcast Beacon messages and multi-hop broadcast Emergency messages triggered by events. In this paper, we analyze the impact of multi-hop broadcast of Emergency messages in C-V2X Mode 4 on the performance of Beacon messages and propose collision probability models for Beacon messages and Emergency messages. The non-periodicity nature and lower broadcast latency requirement of the multi-hop Emergency messages may cause resource allocation conflicts in the resource selection process of C-V2X Mode 4, thus result in forwarding collision of Emergency messages and collision of subsequent Beacon messages. Therefore, we propose several multi-hop broadcast schemes for Emergency messages to reduce the collision probability by assigning independent resource grants for Emergency messages and adjusting the number of forwarding nodes. The performance of Beacon messages and Emergency messages of several different forwarding schemes proposed are simulated. The simulation results show that the proposed broadcasting schemes significantly improve the performance of Beacon messages and Emergency messages.

Vehicle taillight intention detection is an important application for perception and decision making by intelligent vehicles. However, effectively improving detection precision with sufficient real-time performance is a critical issue in practical applications. In this study, a vision-based improved lightweight approach focusing on small object detection with a multi-scale strategy is proposed to achieve application-oriented real-time vehicle taillight intention detection. The proposed real-time detection model is designed based on YOLOv4-tiny, and a spatial pyramid pooling fast (SPPF) module is employed to enrich the output layer features. An additional detection scale is added to expand the receptive field corresponding to small objects. Meanwhile, a path aggregation network (PANet) is used to improve the feature resolution of small objects by constructing a feature pyramid with connections between feature layers. An expanded dataset based on the BDD100K dataset is established to verify the performance of the proposed method. Experimental results on the expanded dataset reveal that the proposed method can increase the average precision (AP) of vehicle, brake, left-turn, and right-turn signals by 1.81, 15.16, 40.04, and 41.53%, respectively. The mean average precision (mAP) can be improved by 24.63% (from 62.20% to 86.83%) at over 70 frames per second (FPS), proving that the proposed method can effectively improve detection precision with good real-time performance.

Detecting traffic signs is an essential task for intelligent and connected vehicles. In this study, a modified model based on the method of You Only Look Once (YOLO) is proposed for detecting different types of Chinese signs, including mandatory, prohibitory, danger warning, guide, and tourist signs. Images of Chinese traffic signs are collected in real scenes and a new dataset is established. The modified model combines the DenseNet method with the YOLOv3 network. Dense blocks are used to strengthen feature propagation and promote feature reuse in those feature layers with low resolution in the YOLOv3 network. Experimental results on the test dataset reveal that the average precision of the modified model, the original YOLOv3, and the YOLOv2 networks are 95.92, 94.59, and 89.39%, respectively. Further comparative analyses that give more detailed experimental evaluation results are conducted on the designed model, including (i) the performance of the designed model based on five categories; (ii) the influence of training set size on the designed model; (iii) the performance of the designed model on occlusion and no object conditions in real scenes. The experimental results show that the modified model is effective at fast and accurate Chinese traffic sign detection in real scenes.

Mixed traffic is a common phenomenon in urban environment. For the mixed traffic situation, the detection of traffic obstacles, including motor vehicle, non-motor vehicle, and pedestrian, is an essential task for intelligent and connected vehicles (ICVs). In this paper, an improved YOLO model is proposed for traffic obstacle detection and classification. The YOLO network is used to accurately detect the traffic obstacles, while the Wasserstein distance-based loss is used to improve the misclassification in the detection that may cause serious consequences. A new established dataset containing four types of traffic obstacles including vehicles, bikes, riders, and pedestrians is collected under different time periods and different weather conditions in urban environment in Wuhan, China. Experiments are performed on the established dataset on Windows PC and NVIDIA TX2, respectively. From the experimental results, the improved YOLO model has higher mean average precision than the original YOLO model and can effectively reduce intolerable misclassifications. In addition, the improved YOLOv4-tiny model has a detection speed of 22.5928 fps on NVIDIA TX2, which can basically realize the real-time detection of traffic obstacles.