Active reconstruction is an intelligent perception method that achieves object modeling with few views and short motion paths by systematically adjusting the parameters of the camera while ensuring model integrity. Part of the object information is always known for vision tasks in real scenes, and it provides some guidance for the view planning. A two-step active reconstruction algorithm based on partial prior information is presented, which includes rough shape estimation phase and complete object reconstruction phase, and both of them introduce the concept of active vision. An information expression method is proposed that can be used to manually initialize the repository according to specific visual tasks, and then the prior information and detected information are used to plan the next best view online until the object reconstruction is completed. The method is evaluated with simulated experiments and the result is compared with other methods.
In the field of aerial image object detection based on deep learning, it’s difficult to extract features because the images are obtained from a top-down perspective. Therefore, there are numerous false detection boxes. The existing post-processing methods mainly remove overlapped detection boxes, but it’s hard to eliminate false detection boxes. The proposed dual non-maximum suppression (dual-NMS) combines the density of detection boxes that are generated for each detected object with the corresponding classification confidence to autonomously remove the false detection boxes. With the dual-NMS as a post-processing method, the precision is greatly improved under the premise of keeping recall unchanged. In vehicle detection in aerial imagery (VEDAI) and dataset for object detection in aerial images (DOTA) datasets, the removal rate of false detection boxes is over 50%. Additionally, according to the characteristics of aerial images, the correlation calculation layer for feature channel separation and the dilated convolution guidance structure are proposed to enhance the feature extraction ability of the network, and these structures constitute the correlation network (CorrNet). Compared with you only look once (YOLOv3), the mean average precision (mAP) of the CorrNet for DOTA increased by 9.78%. Commingled with dual-NMS, the detection effect in aerial images is significantly improved.
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