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.

Public safety and intelligent surveillance systems critically depend on anomaly behavior detection for effective monitoring. In real-world pedestrian detection scenarios, prevalent challenges such as missed detections, complex background interference, and small target sizes hinder accurate anomaly identification. To address these issues, this study introduces YOLO-ABD, a lightweight method for anomaly behavior detection that integrates small target detection and channel shuffling. This method employs YOLOv8n as the baseline model, incorporating a small target detection mechanism in the Head part and utilizing GSConv convolution in the Backbone to enhance perceptual capability. Additionally, the SimAM attention mechanism is integrated to mitigate complex background interference, thereby improving target detection performance. Evaluation on the IITB-Corridor dataset demonstrated mAP50 and mAP50-95 scores of 89.3% and 60.6%, respectively. Generalization testing on the street-view-gdogo dataset further highlighted the superiority of YOLO-ABD over advanced detection algorithms, underscoring its effectiveness and generalizability. With a relatively small parameter count, YOLO-ABD presents an excellent lightweight solution for pedestrian anomaly behavior detection.