Object detection in remote sensing images (RSIs) requires the locating and classifying of objects of interest, which is a hot topic in RSI analysis research. With the development of deep learning (DL) technology, which has accelerated in recent years, numerous intelligent and efficient detection algorithms have been proposed. Meanwhile, the performance of remote sensing imaging hardware has also evolved significantly. The detection technology used with high-resolution RSIs has been pushed to unprecedented heights, making important contributions in practical applications such as urban detection, building planning, and disaster prediction. However, although some scholars have authored reviews on DL-based object detection systems, the leading DL-based object detection improvement strategies have never been summarized in detail. In this paper, we first briefly review the recent history of remote sensing object detection (RSOD) techniques, including traditional methods as well as DL-based methods. Then, we systematically summarize the procedures used in DL-based detection algorithms. Most importantly, starting from the problems of complex object features, complex background information, tedious sample annotation that will be faced by high-resolution RSI object detection, we introduce a taxonomy based on various detection methods, which focuses on summarizing and classifying the existing attention mechanisms, multi-scale feature fusion, super-resolution and other major improvement strategies. We also introduce recognized open-source remote sensing detection benchmarks and evaluation metrics. Finally, based on the current state of the technology, we conclude by discussing the challenges and potential trends in the field of RSOD in order to provide a reference for researchers who have just entered the field.
The Ant Colony Optimization (ACO) is easy to fall into the local optimum and its convergence speed is slow in solving the Travelling Salesman Problem (TSP). Therefore, a Slime Mold-Ant Colony Fusion Algorithm (SMACFA) is proposed in this paper. Firstly, an optimized path is obtained by Slime Mold Algorithm (SMA) for TSP; Then, the high-quality pipelines are selected from the path which is obtained by SMA, and the two ends of the pipelines are as fixed-point pairs; Finally, the fixedpoint pairs are directly applied to the ACO by the principle of fixed selection. Hence, the SMACFA with fixed selection of high-quality pipelines is obtained. Through the test of the chn31 in Traveling Salesman Problem Library (TSPLIB), the result of path length was 15381 by SMACFA, and it was improved by 1.42% than ACO. The convergence speed and algorithm time complexity were reduced by 73.55 and 80.25% respectively. What's more, under the ten data sets of TSPLIB, SMACFA outperformed other algorithms in terms of the path length, convergence speed and algorithm time complexity by comparison experiments. It is fully verified that the performances of SMACFA is superior to others in solving TSP.
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