In the intelligent traffic system, real-time and accurate detections of vehicles in images and video data are very important and challenging work. Especially in situations with complex scenes, different models, and high density, it is difficult to accurately locate and classify these vehicles during traffic flows. Therefore, we propose a single-stage deep neural network YOLOv3-DL, which is based on the Tensorflow framework to improve this problem. The network structure is optimized by introducing the idea of spatial pyramid pooling, then the loss function is redefined, and a weight regularization method is introduced, for that, the real-time detections and statistics of traffic flows can be implemented effectively. The optimization algorithm we use is the DL-CAR data set for end-to-end network training and experiments with data sets under different scenarios and weathers. The analyses of experimental data show that the optimized algorithm can improve the vehicles’ detection accuracy on the test set by 3.86%. Experiments on test sets in different environments have improved the detection accuracy rate by 4.53%, indicating that the algorithm has high robustness. At the same time, the detection accuracy and speed of the investigated algorithm are higher than other algorithms, indicating that the algorithm has higher detection performance.
The adsorption and diffusion behaviors of lithium (Li) in a graphene/blue-phosphorus (G/BP) heterostructure have been investigated using a first principles method based on density functional theory (DFT). The effect of an external electric field on the adsorption and diffusion behaviors has also been investigated. The results show that the adsorption energy of Li on the graphene side of the G/BP heterostructure is higher than that on monolayer graphene, and Li adsorption on the BP side of the G/BP/Li system is slightly stronger than that on monolayer BP (BP/Li). The adsorption energy of Li reaches 2.47 eV, however, the energy barriers of Li diffusion decrease in the interlayer of the G/BP heterostructure. The results mentioned above suggest that the rate performance of the G/BP heterostructure is better than that of monolayer graphene. Furthermore, the adsorption energies of Li atoms in the three different most stable sites, i.e., H, T and H sites, increase by about 0.49 eV, 0.26 eV, and 0.13 eV, respectively, as the electric field intensity reaches 0.6 V Å. The diffusion energy barrier is significantly decreased by an external electric field. It is demonstrated that the external electric field can not only enhance the adsorption but can also modulate the diffusion barriers of Li atoms in the G/BP heterostructure.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.