M-O SiamRPN with Weight Adaptive Joint MIoU for UAV Visual Localization

Abstract

Vision-based unmanned aerial vehicle (UAV) localization is capable of providing real-time coordinates independently during GNSS interruption, which is important in security, agriculture, industrial mapping, and other fields. owever, there are problems with shadows, the tiny size of targets, interfering objects, and motion blurred edges in aerial images captured by UAVs. Therefore, a multi-order Siamese region proposal network (M-O SiamRPN) with weight adaptive joint multiple intersection over union (MIoU) loss function is proposed to overcome the above limitations. The normalized covariance of 2-O information based on1-O features is introduced in the Siamese convolutional neural network to improve the representation and sensitivity of the network to edges. We innovatively propose a spatial continuity criterion to select 1-O features with richer local details for the calculation of 2-O information, to ensure the effectiveness of M-O features. To reduce the effect of unavoidable positive and negative sample imbalance in target detection, weight adaptive coefficients were designed to automatically modify the penalty factor of cross-entropy loss. Moreover, the MIoU was constructed to constrain the anchor box regression from multiple perspectives. In addition, we proposed an improved Wallis shadow automatic compensation method to pre-process aerial images, providing the basis for subsequent image matching procedures. We also built a consumer-grade UAV acquisition platform to construct an aerial image dataset for experimental validation. The results show that our framework achieved excellent performance for each quantitative and qualitative metric, with the highest precision being 0.979 and a success rate of 0.732.

A Monocular-Visual SLAM System with Semantic and Optical-Flow Fusion for Indoor Dynamic Environments

“…The generated feature map shared the convolution layer and the feature pyramid performed different combinations of the extracted stage features. On the one hand, a region proposal network (RPN) [39] was used to determine the foreground and background for binary classification and generate candidate boxes. On the other hand, the generated candidate box corresponded to a pixel in the feature map, which was matched using the RoI Align [40] operation [41].…”

Artificial Intelligence-Based Learning Approaches for Remote Sensing

“…In the contribution by Wen et al [4], the authors creatively presented a spatial endurance criterion to choose 1-O features with wealthier local facts for the computation of 2-O data to guarantee the efficiency of M-O features. To minimize the consequence of inevitable positive/negative sample inequity in target finding, weight-adaptive factors were considered to adapt to the disadvantages of the cross entropy cost.…”