In this paper, in view of the insufficiency of the CenterNet detector’s inability to achieve high real-time performance with high accuracy when performing object detection, we designed a new detector called A_CenterNet. In the detector, we use our newly designed lightweight Hourglass-256 model, and we also use the feature map fusion method we designed, as well as our improved attention mechanism. Through the experimental results on multiple datasets, it can be known that the A_CenterNet proposed in this paper has a competitive advantage compared with some existing classic detectors. A_CenterNet achieves the best speed-accuracy trade-off on the MS COCO dataset, with 44.6 AP at 36 FPS. Compared with CenterNet, A_CenterNet greatly improves the detection speed without loss of detection accuracy.
Autonomous driving has been one of the key factors behind the various technology innovation initiatives in the automotive industry in recent years. A unified driver model controller, which is essential to the design and development of autonomous driving technology, based on fractional-order PIλDμ and internal model control is proposed and studied in this paper. The unified driver model control algorithm utilizes and integrates fractional-order PIλDμ control and internal model control to ensure path tracking capability and driving stability of the vehicle. Matlab/Simulink simulation of the proposed controller indicates that it can effectively improve path tracking capabilities and driving stability.
Although the CenterNet object detection method can be used to achieve high accuracy, it cannot be used to detect objects with overlapping or almost overlapping centers. However, a good trade-off between detection speed and accuracy cannot be achieved when utilizing this method. We improved a series of backbones using CenterNet, output the fusion of multiple feature maps in backbone, and used the feature pyramid network (FPN) mechanism for multiscale object detection. Additionally, we improved the head of the detector and considered adding intersection over union (IoU) branches. Finally, the loss function was improved to improve detection accuracy. Based on the above research, the CenterNet Plus object detection method is proposed in this paper. Through experiments on the COCO dataset, it can be seen that the use of a multiscale FPN mechanism not only solves the problem of center point overlap but also helps improve the accuracy of detection. CenterNet Plus can be used to greatly improve the detection accuracy on the premise of having a higher detection speed.
Autonomous driving has been one of the key factors behind the various technology innovation initiatives in the automotive industry in recent years. A unified driver model controller, which is essential to the design and development of autonomous driving technology, based on fractional-order PIλDμ and internal model control is proposed and studied in this paper. The unified driver model control algorithm utilizes and integrates fractional-order PIλDμ control and internal model control to ensure path tracking capability and driving stability of the vehicle. Matlab/Simulink simulation of the proposed controller indicates that it can effectively improve path tracking capabilities and driving stability.
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