Aiming at the texture processing and noise disturbance problems of simultaneous localization and mapping algorithm for dynamic scenes under the strong static assumption theory, Improved dynamic object tracking based on ant colony clustering (IACDOT) was proposed. The algorithm combines fractional differential and sparse optical flow algorithm to make full use of the weak texture gradient of the image. A dynamic feature search and selection strategy is designed to obtain ant colony clustering, which reduces motion interference and mismatching of dynamic and static features. The experimental results show that the algorithm not only realizes the adaptive selection of pixel gradient order, but also has a better ability to distinguish dynamic disturbance through the clustering of feature selection. It can effectively distinguish motion and static information while retaining more details of weak gradient feature optical flow. The algorithm has a good application prospect in simultaneous localization and mapping system.
Thanks to the development of camera technologies, small unmanned aerial systems (sUAS), it is possible to collect aerial images of field with more flexible visit, higher resolution and much lower cost. Furthermore, the performance of objection detection based on deeply trained convolutional neural networks (CNNs) has been improved significantly. In this study, we applied these technologies in the melon production, where high-resolution aerial images were used to count melons in the field and predict the yield. CNN-based object detection framework-Faster R-CNN is applied in the melon classification. Our results showed that sUAS plus CNNs were able to detect melons accurately in the late harvest season.
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