MEB-YOLO: An Efficient Vehicle Detection Method in Complex Traffic Road Scenes

Rapid and precise vehicle recognition and classification are essential for intelligent transportation systems,and road target detection is one of the most difficult tasks in the field of computer vision.The challenge in real-time road target detection is the ability to properly pinpoint relatively small vehicles in complicated environments.However,because road targets are prone to complicated backgrounds and sparse features,it is challenging to detect and identify vehicle kinds fast and reliably.We suggest a new vehicle detection model called MEB-YOLO,which combines Mosaic and MixUp data augmentation,Efficient Channel Attention(ECA)attention mechanism,Bidirectional Feature Pyramid Network(BiFPN)with You Only Look Once(YOLO)model,to overcome this problem.Four sections make up this model:Input,Backbone,Neck,and Prediction.First,to improve the detection dataset and strengthen the network,MixUp and Mosaic data improvement are used during the picture processing step.Second,an attention mechanism is introduced to the backbone network,which is Cross Stage Par-tial Darknet(CSPDarknet),to reduce the influence of irrelevant features in images.Third,to achieve more sophisticated feature fusion without increasing computing cost,the BiFPN structure is utilized to build the Neck network of the model.The final prediction results are then obtained using Decoupled Head.Experiments demonstrate that the proposed model outperforms several already available detection methods and delivers good detection results on the University at Albany DEtection and TRACking(UA-DETRAC)public dataset.It also enables effective vehicle detection on real traffic monitoring data.As a result,this technique is efficient for detecting road targets.

Research on the Path of Coaching Leadership on Career Success-The Mediating Role of Intraorganizational Employee Navigation Behavior

This study analyzes the role of intraorganizational employee navigation behavior in the relationship between coaching leadership and career success.Based on the theory of social cognition and social capital,593 valid samples are collected.The hypotheses were verified by AMOS,SPSS and Process tools.The research found that coaching leadership can positively predict the career success of employees,coaching leadership and career success is partially mediated by intraorganizational employee navigation behavior.Based on the empirical research results,it is suggested that enterprises strengthen the coaching leadership training,pay attention to stimulate employees to carry out intraorganizational navigation behavior within the organization,and establish a healthy view of professional success,which provides certain theoretical and empirical support for the enterprise management practice.

Study on polyurethane-based porous materials and their adsorption properties

The flexible superhydrophobic thermoplastic polyurethane(TPU)porous material was prepared by heat-induced phase separation method with two cooling steps.The influence of the preparation process on the microstructure of the material was discussed in depth.The microstructure,hydrophobicity and specific surface area of porous TPU materials were analyzed in detail.The surface wettability,separation selectivity,saturated adsorption capacity and adsorption rate,mechanical properties,environmental adaptability and cyclic properties of porous TPU materials were studied.The results show that the TPU-8%porous monolithic material prepared by heat-induced phase separation method shows good performance when the polymer concentration is 8%,the phase separation temperature is 0℃,the phase separation time is 30min,and the mixing solvent ratio is 9:1.