The increasing prevalence of compact UAVs has introduced significant risks to public safety, while traditional drone detection systems are often bulky and costly. To address these challenges, we present TAME, the Temporal Audio-based Mamba for Enhanced Drone Trajectory Estimation and Classification. This innovative anti-UAV detection model leverages a parallel selective state-space model to simultaneously capture and learn both the temporal and spectral features of audio, effectively analyzing propagation of sound. To further enhance temporal features, we introduce a Temporal Feature Enhancement Module, which integrates spectral features into temporal data using residual cross-attention. This enhanced temporal information is then employed for precise 3D trajectory estimation and classification. Our model sets a new standard of performance on the MMUAD benchmarks, demonstrating superior accuracy and effectiveness. The code and trained models are publicly available on GitHub https://github.com/AmazingDay1/TAME.

For infrared (IR) small target detection, we propose a new spatial-temporal tensor (STT) decomposition model based on the tensor Schatten capped$p$norm (TSC$p\text{N}$) and total variation (TV) regularization. First, to explore spatial and temporal information, we construct an STT and introduce the prior weight map. Then, replacing the nuclear norm used to define tensor nuclear norm (TNN) with the nonconvex Schatten capped$p$(SC$p$) norm, we propose the TSC$p\text{N}$to approximate tensor rank and thus recover the low-rank background tensor. Next, to effectively eliminate background clutter from the sparse component, TV regularization is applied to constrain the sparse term. Finally, the proposed model is solved by the alternating direction method of multipliers (ADMM). Experimental results demonstrate the effectiveness and superiority of our proposed method in detecting IR small targets from various challenging sequences.

Abstract In the past few years, contour detection algorithm has made obvious progress with the help of convolutional neural networks. The aim of this paper is to present a novel network connecting low‐ and high‐resolution features to make the network achieving richer feature representation. First, VGG net is used as encoding part with outputting the features of different resolutions, and then the feature maps are combined in some specific resolution with up‐ or down‐sample method. The combining process can be stack step‐by‐step. The proposed network makes the encoding part deeper to extract richer convolutional features. The experiments have shown that the proposed method improves the contour detection performances and outperform some existed convolutional neural networks based methods on BSDS500 and NYUD‐V2 datasets.

-norm of image gradients and total variation (TV) to regularize the latent image and point spread function (PSF), respectively, and combining them with the negative logarithmic Poisson log-likelihood. To solve the problem, we propose an approach which combines the methods of variable splitting and Lagrange multiplier to convert the original problem into three sub-problems, and then design an alternating minimization algorithm which incorporates the estimation of PSF and latent image as well as the updation of Lagrange multiplier into account. We also design a non-blind deconvolution method based on TV regularization to further improve the quality of the restored image. Experimental results on both synthetic and real-world Poissonian blurred images show that the proposed method can achieve restored images of very high quality, which is competitive with or even better than some state of the art methods.

In the mammalian primary visual cortex, the response of the classical receptive field (CRF) to visual stimuli can be suppressed by inhibition of non‐CRF (nCRF) neurons. Although many biologically plausible models based on these centre–surround interaction properties have been proposed, most of these models have failed to account for two important behaviours of neurons in the primary visual cortex (V1). First, saturation properties of neuron response. Second, the properties of fixational eye movements (FEyeMs). In the present study, the authors proposed a biologically motivated counter detection approach based on these properties. The authors’ work is significant in that they utilised a simple threshold method to ensure that CRF responses were observed within a meaningful range, and multichannel filter bank was proposed to simulate the influence of FEyeMs on nCRF. Both methods effectively preserved object contours and inhibition isolated textures. Extensive experiments indicated that the authors’ model can preserve more object contours and suppress more textures than previous biologically based models.

Psychophysical and neurophysiological investigations on the human visual system show that most neurons in the primary visual cortex (V1) possess a non‐classical receptive field (nCRF) region in addition to the CRF region. The nCRF has a modulatory, normally inhibitory, effect on the responses to visual stimuli generated within the CRF. In computational terms, this mechanism suppresses the response to edges in the presence of similar edges in the surroundings. Many computational techniques have been proposed to address the surround suppression mechanism. These methods introduce an inhibition term that is required to suppress the textures and protect the contours. Several studies have found that the spatial summation properties over the receptive fields of retinal X cells are approximately linear, while they are non‐linear for Y cells. Inspired by the visual information processing in the X–Y channel and spatial summation properties of X and Y cells, the authors propose a contour detector using linear and non‐linear modulations based on nCRF suppression. Extensive experimental evaluations demonstrate that their contour detector significantly outperforms other algorithms. The methods proposed in this study are expected to facilitate the development of efficient computational models in the field of machine vision.