In the field of remote sensing image processing, remote sensing image object detection is a crucial undertaking. However, the existing target detection algorithms have a considerable number of model parameters, which results in a slow detection speed that is not conducive to application deployment and real-time detection. Additionally, due to the background complexity of remote sensing images and the large number of small target objects, the performance of existing algorithmic models applied directly with remote sensing images is unsatisfactory. To address the aforementioned issues, this paper proposes an efficient and lightweight architecture based on YOLOv8: AUD-YOLO. In particular, a convolution module, GEConv, based on improved efficient multi-scale attention (EMA), is proposed to replace the original ordinary convolution at the end of the backbone part. This can enhance the accuracy of target detection in remote sensing images while maintaining a lightweight operation. The utilization of dynamic upsampling operators serves to perform upsampling operations with greater accuracy and to more effectively extract image features pertaining to small target objects. Finally, the issue of sample imbalance is addressed by utilising Wise-iou as the position coordinate loss function of the neural network. In the experiments, we validate the effectiveness and robustness of the method using the public remote sensing datasets NWPU VHR-10 and DIOR. The experimental results demonstrate that the AUD-YOLO model proposed in this paper achieves mAP values of 91.1% and 82.7%, which are 1.6% and 1.5% higher than the mAP indexes of the baseline model, and the number of parameters is reduced by 4.7% compared with the original model. We comprehensively consider the number of model parameters and model detection accuracy to make the model more adept at detecting remote sensing images.

The nearest neighbors query problem on road networks constitutes a crucial aspect of location-oriented services and has useful practical implications; e.g., it can locate the k-nearest hotels. However, researches who study road networks still encounter obstacles due to the method’s inherent limitations with respect to object mobility. More popular methods employ indexes to store intermediate results to improve querying time efficiency, but these other methods are often accompanied by high time costs. To balance the costs of time and space, a lightweight flow graph index is proposed to reduce the quantity of candidate nodes, and with this index the results of a kNN query can be efficiently obtained. Experiments on real road networks confirm the efficiency and accuracy of our optimized algorithm.

KSII Transactions on Internet and Information Systems TIIS Paper Details

Outlier detection is an important data mining task, whose target is to find the abnormal or atypical objects from a given dataset. The techniques for detecting outliers have a lot of applications, such as credit card fraud detection and environment monitoring. Our previous work proposed the Cluster‐Based (CB) outlier and gave a centralized method using unsupervised extreme learning machines to compute CB outliers. In this paper, we propose a new distributed algorithm for the CB outlier detection (DACB). On the master node, we collect a small number of points from the slave nodes to obtain a threshold. On each slave node, we design a new filtering method that can use the threshold to efficiently speed up the computation. Furthermore, we also propose a ranking method to optimize the order of cluster scanning. At last, the effectiveness and efficiency of the proposed approaches are verified through a plenty of simulation experiments.