With the increasing prosperity of shipping, effective harbor management has become a requirement. High-resolution optical remote sensing images can effectively monitor and manage ships due to their rich spatial information, clear geometric information, and unique top-down view. However, harbor scenes usually have complex background information and densely distributed ship targets, these two factors increase the difficulty of the algorithm to detect ship targets in remote sensing harbor scenes. With respect to these two issues, this paper proposes a novel harbor ship detection method based on YOLOv6-s with finer-feature fusion which is called F-YOLOv6. First, Extract ship targets from complex ground information. By importing the backbone extraction network efficient re-parameterization (Efficient-Rep) of YOLOv6-s, its efficient extraction ability can help the algorithm to improve the detection accuracy. Second, to improve the detection performance of the algorithm for densely arranged ship targets, the finer pixel aggregation network (F-PAN) is constructed to obtain more ship object position information, and on this basis, it is integrated with the deep high-level semantic information to improve the robustness of the algorithm. Third, to further improve the timeliness of the algorithm, the detection efficiency of the algorithm is improved by deleting some redundant prediction headers. Finally, extensive experiments are carried out on the harbor ship detection (HSD) dataset, which is suitable for densely arranged ship detection, to verify the effectiveness of the proposed method. In addition, the method is verified by the DIOR public dataset, and its generalization is better than other benchmark algorithms.
As the main drug original plant, the accurate identification of poppy has become the key to anti-drug work. Compared with other spatial data collection methods, unmanned aerial vehicle (UAV) image collection technology can monitor across regions in real time, and its flexibility can improve the efficiency of anti-drug work. However, the UAV art remains short of contrastive assessment of recent deep architectures for poppy objects in different environments UAV images. In addition, foggy weather is often present during the most detectable flowering period for poppies. The existence of fog has a certain impact on the anti-drug work that may be carried out at any time. At present, there is a lack of data samples of poppy pictures in foggy weather, so it is difficult to proceed on related research, to solve the aforementioned problems. This paper compares the detection and statistics of poppies in UAV images by state-of-the-art deep learning-based target detection algorithms in different weather environments. These algorithms include YOLO series and CenterNet. To this end, this paper collects and produces a poppy dataset of UAV images in two different weather environments. Through extensive experiments, the performance of state-of-the-art target detection algorithm to detect poppy under different weather conditions is evaluated.
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