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The traditional rearview mirror method cannot fully guarantee safety when driving trucks. RGB and infrared images collected by cameras are used for registration and recognition, so as to achieve the perception of surroundings and ensure safe driving. The traditional scale-invariant feature transform (SIFT) algorithm has a mismatching rate, and the YOLO algorithm has an optimization space in feature extraction. To address these issues, this paper proposes a truck surround sensing technique based on multi-features and an improved YOLOv5 algorithm. Firstly, the edge corner points and infrared features of the preset target region are extracted, and then a feature point set containing the improved SIFT algorithm is generated for registration. Finally, the YOLOv5 algorithm is improved by fusing infrared features and introducing a composite prediction mechanism at the prediction end. The simulation results show that, on average, the image stitching accuracy is improved by 17%, the time is reduced by 89%, and the target recognition accuracy is improved by 2.86%. The experimental results show that this method can effectively perceive the surroundings of trucks, accurately identify targets, and reduce the missed alarm rate and false alarm rate. [ABSTRACT FROM AUTHOR]

Visualizing RNA-protein interactions through structural approaches requires the use of RNA molecules purified to homogeneity. We describe here a simple and effective method, free of acrylamide contamination and without using UV radiation, to separate in vitro synthesized, heterogeneous RNA transcripts (up to ∼15 nucleotides) at single-nucleotide resolution by quaternary-amine anion exchange chromatography. The quality of short RNAs isolated through this method is validated by gel electrophoresis, mass spectrometry, and crystallization with a protein-binding partner., (© The Author(s) 2024. Published by Oxford University Press.)