RGB-T object detection has achieved substantial progress by harnessing the complementary characteristics of diverse spectra for all-weather detection, particularly in critical security applications. However, most state-of-the-art models are based on a feature intermediate fusion structure, which may inadvertently create modality-dependent shortcuts. When a certain location of the input image in the shortcut branch is disturbed, these methods often fail to make accurate decisions, which limits their generalization ability in real-world application scenarios. In this paper, we aim to present a robust multispectral object decision fusion method (called QDFM) to perform effectively on both interferencefree datasets and those affected by sensor interference or failure data. We incorporate evidential deep learning into one-stage object detection and modify the loss function to mitigate the negative effects of class imbalance, allowing the network to naturally express uncertainty in its predictions. Subsequently, quality-aware Generalized D-S evidence theory is employed to reassign background confidence within the belief framework, effectively addressing the one-vote veto problem commonly encountered in traditional D-S combination rules. Experiments conducted on two publicly available datasets demonstrate the effectiveness of our approach in handling both sensor failure scenarios and interference-free data, showcasing significant improvements in robustness and accuracy. QDFM achieves a 10% mAP improvement compared to other advanced methods under modal failure conditions, while outperforming the baseline D-S method by over 2% across various interference scenarios.

Compressive sensing (CS) is a widely utilized technique in hyperspectral imaging front end, and hyperspectral target detection (HTD) in the CS domain is crucial for conserving computational and storage resources on front-end platforms and achieving real-time detection. However, existing HTD methods can only handle reconstructed hyperspectral images. To address this limitation, this study investigates a novel property under hyperspectral CS and proposes an algorithm framework for HTD without reconstruction. The newly proposed property, named restricted distribution property (RDP), is based on assumptions about spectral random vectors and derivations from the CS model. It indicates that the spectral vectors in the compressed hyperspectral follow a deterministic Gaussian distribution under specific conditions and provide a generalized expression for their covariance matrix. Building upon this property, a Kullback–Leibler (KL) divergence subspace distribution estimator is developed, enabling HTD without reconstruction. Experimental result demonstrates that the performance of the proposed detector in compressed hyperspectral data is comparable to or even superior to conventional methods in original hyperspectral images. The result is inspiring for future research.

Hyper-Dimensional Computing (HDC) is an efficient learning paradigm inspired by the high-dimensional properties of human brain. However, existing HDC architectures require large storage overhead to store position and level hyper-vectors, increasing the chip area while also limiting the expansion of dimensions. To solve this problem, we propose a lightweight HDC architecture with Zero Rom overhead, named as ZRHDC. Utilizing a novel parallel encoding method and a rapid random vector generation mechanism based on Linear Feedback Shift Register and shift-flip operations, ZRHDC accelerates encoding process and completely eliminates the ROM overhead. The proposed architecture supports classification tasks with up to 32 categories, 1024 features and 8192 dimensions, achieving 2- $8 \times $ dimensional expansion compared to the state-of-the-art ASIC designs and accuracy improvement in various tasks and training strategies. Moreover, our chip only costs 0.66mm2 at 55nm, achieving 3- $6 \times $ area scaling down. Simulation results show that it consumes 0.771mW when performing the EMG hand-gesture recognition task and merely 0.009mW in standby.