Public safety and intelligent surveillance systems rely on anomaly detection for effective monitoring. In real-world pedestrian detection scenarios, Pedestrians often exhibit various symmetrical features such as body contours, facial features, posture, and clothing. However, the accuracy of pedestrian anomaly detection is affected by factors such as complex backgrounds, pedestrian obstruction, and small target sizes. To address these issues, this study introduced YOLO-ABD, a lightweight method for anomaly behavior detection that integrated small object detection and channel shuffling. This approach enhanced the YOLOv8n baseline model by integrating a small-object detection mechanism at the head and employing the symmetric GSConv convolutional module in the backbone network to improve perceptual capabilities. Furthermore, it incorporated the SimAM attention mechanism to mitigate complex background interference and thus enhance target detection performance. Evaluation on the IITB-Corridor dataset showed mAP50 and mAP50-95 scores of 89.3% and 60.6%, respectively. Generalization testing on the street-view-gdogo dataset further underscored the superiority of YOLO-ABD over advanced detection algorithms, demonstrating its effectiveness and generalization capabilities. With relatively fewer parameters, YOLO-ABD provided an excellent lightweight solution for pedestrian anomaly detection.

Currently, there are several issues in China’s rural property rights trading, including poor credit reporting, low transaction efficiency, non-standardized transaction contracts, and high costs for rights protection. However, blockchain technology, known for its convenience, security, and traceability, is highly compatible with the requirements of standardization, convenience, and agility in rural property rights trading. Therefore, we propose a system framework for rural property rights trading based on the Hyperledger Fabric consortium blockchain. This framework significantly enhances transaction efficiency, improves the security of rural property rights trading, effectively resolves contract disputes in property rights transactions, and promotes rural revitalization and development. Furthermore, we design the information data structure on the blockchain, present the information flow, and develop smart contracts that utilize automation capabilities to automatically summarize, analyze, and issue alerts based on on-chain data, enabling risk assessment for both buyers and sellers. Additionally, we introduce an improved PBFT consensus algorithm called CA-PBFT, which is based on the PBFT consensus protocol and integrates a scoring model and simplified consensus protocol. CA-PBFT efficiently reduces transaction response latency and improves information throughput. Finally, we develop a prototype system for rural property rights trading and perform a performance comparison and analysis of CA-PBFT against other PBFT-based consensus algorithms in this prototype system. Experimental results demonstrate the feasibility of the prototype system framework for rural property rights trading.

Supervision of Livelihood Issues (SLI) refers to the supervision and participation of the general public in government actions and public affairs, with a particular focus on issues closely related to people’s lives. However, current regulatory systems often suffers from information imbalances, low authenticity, and credibility. To address these challenges, this study proposes a blockchain-based SLI framework that leverages the decentralized, tamper-proof, traceable, and transparent nature of blockchain technology to provide a reliable platform for SLI. Furthermore, we designed an information data structure on the blockchain and presented the information flow process. We developed smart contracts that utilize automation capability to automatically aggregate, analyze, and publish warnings based on on-chain data, enabling the evaluation of relevant departments’ performance. Moreover, in the context of SLI applications, we propose an optimized consensus algorithm called the SLI-PBFT. This algorithm is based on the PBFT consensus protocol and incorporates dynamic scalability mechanisms, scoring models, and simplified consistency protocols to enhance the consensus efficiency of SLI applications. Finally, the experiments were conducted. We developed an SLI prototype system and compared and analyzed the performance of SLI-PBFT with other PBFT-based consensus algorithms within the SLI prototype system. The experimental results demonstrated the feasibility of the SLI framework. Moreover, SLI-PBFT exhibits significant improvements in throughput and reduced latency compared to other algorithms in the context of SLI applications. In addition, they possess strong fault tolerances and security capabilities. This study provides theoretical and practical guidance for the SLI in real-world scenarios.

Aiming at the situation that the existing visible and infrared images fusion algorithms only focus on highlighting infrared targets and neglect the performance of image details, and cannot take into account the characteristics of infrared and visible images, this paper proposes an image enhancement fusion algorithm combining Karhunen-Loeve transform and Laplacian pyramid fusion. The detail layer of the source image is obtained by anisotropic diffusion to get more abundant texture information. The infrared images adopt adaptive histogram partition and brightness correction enhancement algorithm to highlight thermal radiation targets. A novel power function enhancement algorithm that simulates illumination is proposed for visible images to improve the contrast of visible images and facilitate human observation. In order to improve the fusion quality of images, the source image and the enhanced images are transformed by Karhunen-Loeve to form new visible and infrared images. Laplacian pyramid fusion is performed on the new visible and infrared images, and superimposed with the detail layer images to obtain the fusion result. Experimental results show that the method in this paper is superior to several representative image fusion algorithms in subjective visual effects on public data sets. In terms of objective evaluation, the fusion result performed well on the 8 evaluation indicators, and its own quality was high.

Traditional street quality evaluations are often subjective and limited in scale, failing to capture the nuanced and dynamic aspects of urban environments. This paper presents a novel and data-driven approach for objective and comprehensive street quality evaluation using street view images and semantic segmentation. The proposed SP-UNet (Spatial Pyramid UNet) is a multi-scale segmentation model that leverages the power of VGG16, SimSPPF (Simultaneous Spatial and Channel Pyramid Pooling), and MLCA (Multi-Level Context Attention) attention mechanisms. This integration effectively enhances feature extraction, context aggregation, and detail preservation. The model’s average intersection over union, Mean Pixel Accuracy, and overall accuracy achieving improvements of 5.83%, 6.52%, and 2.37% in mIoU, Mean Pixel Accuracy (mPA), and overall accuracy, respectively. Further analysis using the CRITIC method highlights the model’s strengths in various street quality dimensions across different urban areas. The SP-UNet model not only improves the accuracy of street quality evaluation but also offers valuable insights for urban managers to enhance the livability and functionality of urban environments.