Accurate and efficient detection of pavement distress is essential for maintaining road infrastructure safety and functionality, yet artificial intelligence-based detection methods are constrained by data scarcity, class imbalance, and prohibitive annotation costs. To address these fundamental challenges, this paper proposes CrackMaskNet, a novel diffusion-based generative model capable of producing high-quality, class-controllable pavement distress images along with pixel-level segmentation masks. The model integrates a forward diffusion process to encode noisy representations and a reverse diffusion process guided by the Dual-Output Guided Synthesis module to simultaneously generate annotated distress images. Extensive experiments demonstrate the superior performance of CrackMaskNet compared to state-of-the-art generative methods, achieving high visual fidelity with an FID score of 37.6 and enhancing detection and segmentation accuracy across multiple crack categories. Furthermore, CrackMaskNet exhibits strong generalization capabilities, extending its applicability to other domains, such as road pothole generation and person re-identification in traffic monitoring systems. The proposed method provides an effective and scalable solution for enhancing pavement distress datasets, reducing annotation costs, and improving the robustness of downstream detection and segmentation models, significantly advancing automated pavement condition assessment systems.

Team-based combat scenarios are prevalent in various real-world applications like video gaming. Analyzing tactics in these scenarios is essential for gaining insights into game processes and improving combat behaviors. The decision-making data in team-based combat include character actions, movement trajectories, and event sequences. Existing studies face challenges in visualizing and analyzing combat tactics due to the complexity and the multifaceted characteristics of the decision-making data. To address these challenges, we introduce TactiVis, a visual analytics system designed for analyzing combat decision-making behavior. Using MOBA game as a representative case of team-based combat, TactiVis adopts a macro-to-micro tactics visual analytics framework consisting of three stages: match-level analysis, event-level understanding, and character-level comparison. In the TactiVis system, we introduce the v-storyline visualization, which encodes positions along the vertical axis to reveal tactical patterns. Case studies and a usability study demonstrate the utility and usability of TactiVis for helping analysts understand combat patterns and analyze tactics.

The detection of rare objects within traffic environments is critically important for the safety and reliability of autonomous driving systems. However, the scarcity of sufficient data and the long-tail effect associated with rare objects pose significant challenges to their detection. To address this issue, this study introduces a Conditional Diffusion Model, the RareGenDiffuse Model, which generates images of rare objects by taking traffic background images and corresponding bounding box images as inputs. Based on diffusion models, the RareGenDiffuse Model enables control over the category and location of the generated rare objects by adjusting the bounding box images. Experimental results demonstrate that the proposed model offers the advantages of high clarity and controllability. Utilizing the generated rare object dataset significantly enhances the detection outcomes for rare objects. The methodology proposed in this study can effectively reduce the efforts required for data acquisition and labeling, presenting a valuable solution to the challenges of rare object detection in traffic scenarios.

Fine-grained vehicle classification of make and model recognition is of practical significance and can be widely applied in the tasks such as traffic perception and control. To date, vision-based vehicle make and model recognition methods have improved through the years; however, they often fail in low-visibility scenes. This paper proposes a fine-grained vehicle model recognition framework based on magnetic fingerprints instead of videos from surveillance cameras. The magnetic fingerprint refers to the pattern of magnetic field data obtained when vehicles pass magnetic sensors, which have less sensitivity and more robustness towards low-visibility scenes than surveillance cameras. Specifically, the proposed framework first utilizes adversarial autoencoders to extract the magnetic fingerprint from the raw magnetic field data, keeping detailed information for fine-grained vehicle model recognition as complete as possible. Then, an AdaBoostSVM algorithm is used to identify vehicle models from the magnetic fingerprint and further alleviate the hard-negative issue to reduce false classifications. Massive experimental results show that the proposed fine-grained vehicle magnetic fingerprint/AdaBoostSVM method achieves state-of-the-art results on fine-grained vehicle make and model recognition. The average precision reached 94.3% and the average recall reached 94.2% in the dataset containing 36 vehicle models in total. Notably, the proposed method outperforms surveillance camera methods, especially in low-visibility scenes, making it a valuable addition to future intelligent transportation systems.

Road crack segmentation from on-board cameras is a highly desirable yet challenging task for road condition inspection and maintenance. However, existing methods trained on small-scale datasets present limited performance from such challenging perspectives due to the lack of sufficient prior knowledge and effective generalizability. To address this limitation, this paper incorporates a vision foundation model named Segment-Anything-Model (SAM) and fully leverages its rich prior knowledge and strong generalizability to achieve crack segmentation. Also, we construct a customized crack segmentation dataset shot from on-board cameras. Considering the direct use of SAM might not correctly segment cracks, some lightweight and learnable crack adaptation layers are developed and integrated into SAM’s image encoder, which take the patch embeddings of the input image and its high-frequency components within road regions as joint inputs. During training, the parameters of the crack adaptation layers are fine-tuned to acquire domain-specific knowledge, while the parameters of the image encoder remain frozen, preserving SAM’s rich prior knowledge. Additionally, a sparse prompt generation method is proposed based on the high-frequency components within road regions, which guides the SAM model to better focus on high-frequency regions that may contain cracks. Experimental results demonstrate that the proposed framework achieves state-of-the-art performance, with an improved average precision of 8.14% compared to Mask2Former. Furthermore, the parameter-efficient transfer learning framework significantly reduces the number of parameters requiring fine-tuning, thereby improving efficiency and reducing training costs. The dataset proposed in this paper is available athttps://github.com/TRMetaGroup/CrackSeg.

Multi-agent deep reinforcement learning (MADRL) has been widely used in many scenarios such as robotics and game AI. However, existing methods mainly focus on the optimization of agents' micro policies without considering the macro strategy. As a result, they cannot perform well in complex or sparse reward scenarios like the StarCraft Multi-Agent Challenge (SMAC) and Google Research Football (GRF). To this end, we propose a hierarchical MADRL framework called "HiMacMic" with dynamic asynchronous macro strategy. Spatially, HiMacMic determines a critical position by using a positional heat map. Temporally, the macro strategy dynamically decides its deadline and updates it asynchronously among agents. We validate HiMacMic in four widely used benchmarks, namely: Overcooked, GRF, SMAC and SMAC-v2 with nine chosen scenarios. Results show that HiMacMic not only converges faster and achieves higher results than ten existing approaches, but also shows its adaptability to different environment settings.