In recent years, frequent forest fires have plagued countries all over the world, causing serious economic damage and human casualties. Faster and more accurate detection of forest fires and timely interventions have become a research priority. With the advancement in deep learning, fully convolutional network architectures have achieved excellent results in the field of image segmentation. More researchers adopt these models to segment flames for fire monitoring, but most of the works are aimed at fires in buildings and industrial scenarios. However, there are few studies on the application of various fully convolutional models to forest fire scenarios, and comparative experiments are inadequate. In view of the above problems, on the basis of constructing the dataset with remote-sensing images of forest fires captured by unmanned aerial vehicles (UAVs) and the targeted optimization of the data enhancement process, four classical semantic segmentation models and two backbone networks are selected for modeling and testing analysis. By comparing inference results and the evaluation indicators of models such as mPA and mIoU, we can find out the models that are more suitable for forest fire segmentation scenarios. The results show that the U-Net model with Resnet50 as a backbone network has the highest segmentation accuracy of forest fires with the best comprehensive performance, and is more suitable for scenarios with high-accuracy requirements; the DeepLabV3+ model with Resnet50 is slightly less accurate than U-Net, but it can still ensure a satisfying segmentation performance with a faster running speed, which is suitable for scenarios with high real-time requirements. In contrast, FCN and PSPNet have poorer segmentation performance and, hence, are not suitable for forest fire detection scenarios.

Arterial-branch intersections are important components of urban road network but are greatly ignored of its role in maintaining an efficient traffic operation in regional networks. Arterial-branch intersections are generally featured with significant fluctuations in the flow ratio of the branch road to the arterial road. So, in order to adapt the signal timing to this kind of intersection, an optimization control algorithm based on fuzzy control and nonlinear programming (FCNP) was proposed. To verify this optimization algorithm, the Python and Vissim joint simulation was employed. Prior to the simulation, traffic flow data were collected in 12 consecutive hours at an arterial-branch intersection in China. The simulation results show that, after signal timing optimization with FCNP, the average vehicle queue length and delay reduced 25.8% and 17.3%, respectively, when compared with the performance of the traffic-actuated control, which also outperformed previous equivalent research. Besides, the overall operation of the intersection was verified to be greatly improved and stabilized by using the proposed algorithm. The findings of this study provide a reasonable solution of distributing the right-of-way at arterial-branch intersections and suggest the advantage of combining fuzzy control and nonlinear programming in dealing with the signal timing optimization.

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