Low-Light Enhancer for UAV Night Tracking Based on Zero-DCE++

Unmanned aerial vehicle (UAV) target tracking tasks can currently be successfully completed in daytime situations with enough lighting, but they are unable to do so in nighttime scenes with inadequate lighting, poor contrast, and low signal-to-noise ratio. This letter presents an enhanced low-light enhancer for UAV nighttime tracking based on Zero-DCE++ due to its ad-vantages of low processing cost and quick inference. We developed a light-weight UCBAM capable of integrating channel information and spatial features and offered a fully considered curve projection model in light of the low signal-to-noise ratio of night scenes. This method significantly improved the tracking performance of the UAV tracker in night situations when tested on the public UAVDark135 and compared to other cutting-edge low-light enhancers. By applying our work to different trackers, this search shows how broadly applicable it is.

Forest Fire Smoke Detection Method Based on MoAm-YOLOv4 Algorithm

To improve the performance of the forest fire smoke detection model and achieve a better balance between detection accuracy and speed, an improved YOLOv4 detection model (MoAm-YOLOv4) that combines a lightweight network and attention mechanism was proposed. Based on the YOLOv4 algorithm, the backbone network CSPDarknet53 was replaced with a lightweight network MobilenetV1 to reduce the model’s size. An attention mechanism was added to the three channels before the output to increase its ability to extract forest fire smoke effectively. The algorithm used the K-means clustering algorithm to cluster the smoke dataset, and obtained candidate frames that were close to the smoke images;the dataset was expanded to 2000 images by the random flip expansion method to avoid overfitting in training. The experimental results show that the improved YOLOv4 algorithm has excellent detection effect. Its mAP can reach 93.45%, precision can get 93.28%, and the model size is only 45.58 MB. Compared with YOLOv4 algorithm, MoAm-YOLOv4 improves the accuracy by 1.3% and reduces the model size by 80% while sacrificing only 0.27% mAP, showing reasonable practicability.

Scalable fast benchmarking for individual quantum gates with local twirling

With the development of controllable quantum systems,fast and practical characterization of multi-qubit gates has become essential for building high-fidelity quantum computing devices.The usual way to fulfill this requirement via randomized benchmarking demands complicated implementation of numerous multi-qubit twirling gates.How to efficiently and reliably estimate the fidelity of a quantum process remains an open problem.This work thus proposes a character-cycle benchmarking protocol and a character-average benchmarking protocol using only local twirling gates to estimate the process fidelity of an individual multi-qubit operation.Our protocols were able to characterize a large class of quantum gates including and beyond the Clifford group via the local gauge transformation,which forms a universal gate set for quantum computing.We demonstrated numerically our protocols for a non-Clifford gate—controlled-(T X)and a Clifford gate—five-qubit quantum errorcorrecting encoding circuit.The numerical results show that our protocols can efficiently and reliably characterize the gate process fidelities.Compared with the cross-entropy benchmarking,the simulation results show that the character-average benchmarking achieves three orders of magnitude improvements in terms of sampling complexity.