The current stereo warehouse in the cold chain logistics industry faces problems such as high product loss rate, severe temperature deviations, and low level of informatization. To ensure the food quality and safety of cold chain logistics warehouses, and achieve cost reduction and efficiency improvement, this study focuses on the operational scenarios of stereo warehouses in cold chain logistics, and designs a five-dimensional digital twin model with full mapping and multidimensional simulation. By integrating heterogeneous data from multiple sources, it achieves real-time visualization and monitoring of all elements in warehouses, and proposes a model optimization strategy that supports system-linked decision-making. This strategy intelligently adjusts the load and thermal distribution in the temperature-controlled zones of warehouses to maximize operational efficiency and reduce operating costs. Using this approach, a digital twin system for cold chain logistics warehouses was constructed. By comparing the warehouse operation data before and after optimization, the loss rate of fresh vegetables under the same storage conditions was reduced by 25-30%. This system achieves precise mapping and coordinated operation of real-time data from cold chain logistics warehouses and dynamic virtual models. The practical application of this solution is demonstrated in a cold-chain warehouse of a supply chain company in Guangdong, forming an integrated control model for cold chain logistics warehouses. This model provides a reference for the application of digital twin technology in the operation, scheduling, decision-making, and collaborative operations of cold chain logistics warehouses and offers new possibilities for establishing improved packaging for perishable products and ensuring cold chain food quality and safety.

As the latest video coding standard, Versatile Video Coding (VVC) is highly efficient at the cost of very high coding complexity, which seriously hinders its practical application. Therefore, it is very crucial to improve its coding speed. In this paper, we propose a learning-based fast split mode (SM) and directional mode (DM) decision algorithm for VVC intra prediction using a deep learning approach. Specifically, given the observation that the SM distributions of coding units (CUs) of different sizes are significantly distinct, we first design the neural networks separately and train the SM models for all CUs of different sizes to obtain the probability of SMs and skip the unlikely ones. Second, given a similar observation that the DM distributions of CUs of different sizes are distinct, we design neural networks to train the DM models for all CUs of different sizes separately to obtain the probabilities of DMs, and then adaptively select candidate DMs based on probabilities of their located SMs. Third, after an SM is checked, we select its probability, residual coefficients, rate-distortion (RD) cost, etc. as features, and design a lightweight neural network (LNN) model to early terminate SM selection. Experimental results demonstrate that the proposed algorithm can reduce the encoding time of VVC by 70.73% with 2.44% increase in Bjontegaard delta bit-rate (BDBR) on average.

Currently, screen content video applications are increasingly widespread in our daily lives. The latest Screen Content Coding (SCC) standard, known as Versatile Video Coding (VVC) SCC, employs a quad-tree plus multi-type tree (QTMT) coding structure for Coding Unit (CU) partitioning and screen content Coding Modes (CMs) selection. While VVC SCC achieves high coding efficiency, its coding complexity poses a significant obstacle to the further widespread adoption of screen content video. Hence, it is crucial to enhance the coding speed of VVC SCC. In this paper, we propose a fast mode and splitting decision for Intra prediction in VVC SCC. Specifically, we initially exploit deep learning techniques to predict content types for all CUs. Subsequently, we examine CM distributions of different content types to predict candidate CMs for CUs. We then introduce early skip and early terminate CM decisions for different content types of CUs to further eliminate unlikely CMs. Finally, we develop Block-based Differential Pulse-Code Modulation (BDPCM) early termination and CU splitting early termination to improve coding speed. Experimental results demonstrate that the proposed algorithm improves coding speed on average by 41.14%, with the BDBR increasing by 1.17%.

Salient Object Detection (SOD) are widely used in quality inspection scenarios, such as rail surface detection.Recent studies have proven that incorporating complementary information like depth and thermal images is conducive to SOD. Effectively leveraging the advantages of each modality while eliminating inter-modality noise in multi-level fusion has been a research hotspot. Most existing works use convolution and attention mechanisms for modality interaction but overlook semantic similarity during fusion, leading to poor performance in some challenging scenarios. In this paper, inspired by psychology studies on human vision system (HVS), we propose a Dynamic Feature Integration Network (DFINet) that simulates the correlation mechanism between human attention and semantics for RGB-D and RGB-T SOD. Specifically, to better capture the modal-specific features in semantics, we first employ a multi-granularity-based pre-segmentation method, namely the Pre-segmentation Injection Module (PIM), to enhance and preserve the modal-specific features at different layers of the backbone network. Then, a Dynamic Feature Fusion Module (DFFM) is devised to simulate the mechanism of HVS where specific semantic regions gain more attention. This module evaluates the semantic similarity between different modal features and determines the weights for each modal feature in the fusion. The encoded multi-modal features are fed into a staircase decoder which can retain deep semantic information to boost accuracy. Extensive experiments on RGB-D and RGB-T SOD datasets validate that our proposed cognition-inspired framework has excellent and competitive performance with good generalization and robustness.

In recent years, AI-Generated Images (AIGIs) have attracted significant attention and shown great potential in various applications, including entertainment, advertisement, education, and product design. Driven by this trend, various Text-to-Image (T2I) models are developed. However, the quality of AIGIs produced by these models varies widely, with many low-quality images failing to meet human aesthetic standards. Consequently, research into both subjective and objective Image Quality Assessment (IQA) methods for AIGIs is crucial. In this paper, we introduce a dataset called AIGI-IQAD, designed to enhance our understanding of human aesthetic preferences for AIGIs. The dataset contains 2,880 AIGIs generated by 8 T2I models using 360 deliberately designed text prompts. Further, we conducted subjective experiments to gather ratings from both aesthetic quality and text-image consistency. Building on this dataset, we propose a model named Question-guided Multimodal Interaction Network (QMI-Net) for evaluating AIGIs. QMI-Net assesses human preferences for AIGIs by focusing on both aesthetic quality and text-image consistency. Specifically, QMI-Net uses a question-answering approach to guide Multimodal Large Language Models (MLLMs) in generating detailed aesthetic and similarity information. The Visual and Aesthetic Feature Fusion Module (VAFFM) then fuses the aesthetic features with the visual features extracted by Contrastive Language-Image Pre-training (CLIP) to obtain more comprehensive aesthetic quality features. Comprehensive experiments demonstrate that state-of-the-art performance is achieved by QMI-Net on our AIGI-IQAD and three other public datasets.The AIGI-IQAD datasets and QMI-Net will be released at https://github.com/ctxya1207/QMI-Net.