The fundamentality of the traveling salesman problem (TSP) is the choice of an edge in the next step. This paper proposes a concept of link degree, which can display the potentiality of an edge to belong to the shortest Hamiltonian cycle in a more effectively manner and, on this basis, it presents a greedy algorithm for the TSP. Meanwhile, some relevant theorems and conjectures as well as some problems triggered are discussed as well.

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In October 2010, a new British study said that bumblebees fluttering from flower to flower showed the ability to easily solve the “traveler salesman problem.” There is no good explanation for how the bumblebee solved the problem. In this paper, a simple and efficient algorithm is proposed to explain how bumblebee solves the traveling salesman problem.

Satellite remote sensing images have the problems of large image scale, dense arrangement of segmentation targets and different directions. And the specifications and clarity are far from the natural images, resulting in difficulty in feature extraction. Therefore, the detection accuracy of Mask-RCNN is poor when applied to remote sensing image instance segmentation. In this regard, an improved Mask-RCNN algorithm is proposed. First, a deformable convolution kernel is introduced into the back bone network to adaptively change the theoretical receptive field. On this basis, the FPN module is modified, and feature layered fusion is introduced to further improve the feature extraction capability of the model. At the same time, the Soft-NMS algorithm is used to screen the target candidate frame. Validated using the iSAID dataset. The experimental results based on the data set show that the improved algorithm has higher detection accuracy than the original algorithm, and the missed detection rate is reduced.

With the continuous development of satellite technology, the acquisition and processing of satellite remote sensing data have become increasingly common. However, at present, satellites still use a mode of transmitting data back to the ground and utilizing ground CPU/GPU platforms for intelligent processing, which is inefficient due to the process of data transmission between the satellite and the ground. This article studies the application method and implementation of real-time remote sensing image recognition by integrating the Yulong810 on-board intelligent module on a satellite based on the YOLOv3 object detection algorithm. In response to the actual needs of satellite remote sensing image recognition, the algorithm is optimized based on the Yulong810 on-board intelligent module, which improves the recognition efficiency and reduces power consumption while ensuring the accuracy of remote sensing image recognition. By comparing with ground CPU/GPU platforms, the results show that the Yulong810 on-board intelligent module has lower power consumption and higher efficiency, and the recognition accuracy is comparable to that of ground platforms, which can completely replace ground remote sensing image detection equipment when integrated into satellites. By integrating the module into the satellite, real-time intelligent processing can be achieved in orbit, improving the efficiency of satellite remote sensing image recognition.