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From the perspective of computer vision, both visual saliency and object detection have attracted hot attention in the field of traffic scene perception. However, these two tasks are often seen as independent missions, and their correlations have rarely been explored. In real driving scenarios, drivers mainly care about the salient objects closely related to the current driving task under the guidance of visual selective attention. This process highly integrates saliency perception and object detection, leading to efficient and quick decision-making, thus achieving safe driving. In this study, with reference to human drivers’ perception of traffic scenes, we focus on detecting fixated objects within the regions attracting the drivers’ attention. Firstly, we build a new fixated object detection dataset based on drivers’ fixations, which can serve as a benchmark for studying traffic object detection from the driver’s point of view. Then, we propose a fixated object detection model based on saliency prior, named FOD-Net. FOD-Net takes advantage of the predicted salient regions as saliency priors to guide the detection of the fixated objects that are closely relevant to the driving task, thus improving detection accuracy. Experimental results on the proposed dataset show that FOD-Net achieves a mAP value of 78.4% with small model parameters, which is higher than other state-of-the-art models. Our work combines the driver’s attention mechanism with object detection to narrow the gap between visual saliency and object detection in traffic scenes, showing potential supplemental or referential value for developing high-intelligence assisted/automatic driving systems. The dataset and code are available in https://github.com/YiShi701/Fixated-object-detection.

Fluorescent proteins (FPs) provide a ratiometric readout for quantitative assessment of the destination of internalized biomolecules. FP-inspired peptide nanostructures that can compete with FPs in their capacity are the most preferred building blocks for the synthesis of fluorescent soft matter. However, realizing a ratiometric emission from a single peptide fluorophore remains exclusive since multicolor emission is a rare property in peptide nanostructures. Here, we describe a bioinspired peptidyl platform for ratiometric intracellular quantitation by employing a single ferrocene-modified histidine dipeptide. The intensiometric ratio of green to blue fluorescence correlates linearly with the concentration of the peptide by three orders of magnitude. The ratiometric fluorescence of the peptide is an assembly-induced emission originating from hydrogen bonds and aromatic interactions. Additionally, modular design enables ferrocene-modified histidine dipeptides to use as a general platform for the construction of intricate peptides that retain the ratiometric fluorescent properties. The ratiometric peptide technique promises flexibility in the design of a wide spectrum of stoichiometric biosensors for quantitatively understanding the trafficking and subcellular fate of biomolecules.

The recent discovery of 2D magnets has revealed various intriguing phenomena due to the coupling between spin and other degrees of freedoms (such as helical photoluminescence, nonreciprocal SHG). Previous research on the spin-phonon coupling effect mainly focuses on the renormalization of phonon frequency. Here we demonstrate that the Raman polarization selection rules of optical phonons can be greatly modified by the magnetic ordering in 2D magnet CrI3. For monolayer samples, the dominant A1gpeak shows an abnormally high intensity in the cross-polarization channel at low temperatures, which is forbidden by the selection rule based on the lattice symmetry. For the bilayer, this peak is absent in the cross-polarization channel for the layered antiferromagnetic (AFM) state and reappears when it is tuned to the ferromagnetic (FM) state by an external magnetic field. Our findings shed light on exploring the emergent magneto-optical effects in 2D magnets.