As a part of computer vision, object detection is crucial for traffic management, emergency response, autonomous driving vehicles, and smart cities. Despite significant progress in object detection, detecting small objects remains challenging due to their low resolution, which results in little visual information, difficulty in extracting discriminative features, and susceptibility to interference from environmental factors. To address these challenges, we propose SF-DETR, a new model designed specifically for scenarios with small targets. Firstly, We designed a new backbone network that uses partial channel self-attention to replace the backbone network of RTDETR, which can capture both local and global contextual information for extracting and enhancing input features, thereby enhancing the perception of small targets. Secondly, in order to enhance the feature representation ability of the model and better preserve the details of small objects, we proposed Feature Enhancement and Refinement (FER) module, which incorporates a bidirectional fusion mechanism between high-resolution and low-resolution features, allowing for more comprehensive information transfer between features and further improving the effect of multi-scale feature fusion. Finally, we introduce an efficient IoU method (PIoU) which simplifies the computation, speeds up the convergence, and improves the detection accuracy. SF-DETR significantly improves the detection of small targets, outperforming widely used models on various metrics, while significantly reducing model parameters and computational costs compared to RT-DETR. Compared to RTDETR-R34, our model has improved the mAP@50 and [email protected]:0.95 by 3.3% and 2.4% respectively on the Visdrone2019 test set.

Abstract Marine Internet of Things (IOT) is the use of Internet technology to connect various sensing devices at sea, so as to integrate maritime information and realize the monitoring and systematic management of complex data at sea. The marine environment is complex and changeable, and marine disasters occur frequently, such as red tides. Due to the sudden and destructive nature of red tide, it plays a pivotal role to monitor the occurrence of the red tide for the marine IoT, where machine learning has been widely used to predict red tides. However, they were rarely able to catch the sudden change of chlorophyll‐a, which has important practical significance for predicting the occurrence of red tide. In order to deal with the above problems, this paper proposes the triple‐stage attention‐based recurrent neural network, which can enhance the representation ability of input sequences, selectively capture dynamic spatial correlations between input multi‐channel observations in the input sequence, meanwhile adaptively capturing dynamic temporal correlations between different time intervals in the input sequence. The results show that this method outperforms the state‐of‐art baseline methods here.

Strengthening the supervision of bidding business is an important measure to optimise the business environment of bidding and ensure fair competition among enterprises. The existing blockchain-based bidding platform has already achieved the uploading of simple business, but with the development of the bidding industry, single-modal data storage is not enough to support the credible traceability of business processes. Therefore, to address the problems of multimodal data uploading, storage space limitation and performance in the bidding field, and combined with the node complexity of the bidding scenario, we design and propose a bidding management platform for cloud-chain convergence(BMPC3), in which ChainMaker serves as the implementation platform. In this alliance chain, we meet different business logic requirements by designing specific smart contracts and storage structures. According to different business data types, the ledger storage method is optimised to meet the on-chain storage requirements of multimodal data such as images, videos, PDFs, etc. Meanwhile, we explore the blockchain architecture and system configuration, and propose an optimisation scheme, which effectively improves the system transaction throughput. The final experiment shows that the transaction throughput of the system can reach 3300TPS, which can meet the application requirements.

In order to ensure food safety, it is required for personnel to wear hats and masks during food handling processes. To accurately detect the wearing status of kitchen staff, the ARP-YOLO model is proposed. Firstly, images are obtained from multiple kitchens and angles to construct a dataset reflecting the wearing status of hats and masks. To simulate more complex kitchen environments, Gaussian noise is added to the data and lighting conditions are adjusted for data augmentation. Lighting conditions in the kitchen can affect detection, causing the same target to exhibit different shapes and features under different lighting conditions, leading to missed detections. To address the above issues, we propose ARCA (Adaptive-Residual-Coordinate-Attention), which uses residual connections to strengthen attention to important features while preserving original features, and employs adaptive convolution reduction to reduce module parameters. To improve target localization accuracy, P2 detection layers are added in the Neck to obtain more accurate target position information. The ARP-YOLO model demonstrates significant improvements over the baseline model, with a 13.6% increase in Recall, allowing for more effective target capture and reduced missed detection risk. Additionally, [email protected] has increased by 10%, enhancing target localization accuracy. The F1-Score has also increased by 7.4%, better balancing the relationship between Precision and Recall. To validate the effectiveness of the model, comparative experiments with other models are conducted, showing that ARP-YOLO model's Recall and Average Precision(AP) are higher than those of other models.