6 research outputs found
Single Image Deraining via Rain-Steaks Aware Deep Convolutional Neural Network
It is challenging to remove rain-steaks from a single rainy image because the
rain steaks are spatially varying in the rainy image. This problem is studied
in this paper by combining conventional image processing techniques and deep
learning based techniques. An improved weighted guided image filter (iWGIF) is
proposed to extract high frequency information from a rainy image. The high
frequency information mainly includes rain steaks and noise, and it can guide
the rain steaks aware deep convolutional neural network (RSADCNN) to pay more
attention to rain steaks. The efficiency and explain-ability of RSADNN are
improved. Experiments show that the proposed algorithm significantly
outperforms state-of-the-art methods on both synthetic and real-world images in
terms of both qualitative and quantitative measures. It is useful for
autonomous navigation in raining conditions
Water Column Detection Method at Impact Point Based on Improved YOLOv4 Algorithm
For a long time, the water column at the impact point of a naval gun firing at the sea has mainly depended on manual detection methods for locating, which has problems such as low accuracy, subjectivity and inefficiency. In order to solve the above problems, this paper proposes a water column detection method based on an improved you-only-look-once version 4 (YOLOv4) algorithm. Firstly, the method detects the sea antenna through the Hoffman line detection method to constrain the sensitive area in the current detection image so as to improve the accuracy of water column detection; secondly, density-based spatial clustering of applications with noise (DBSCAN) + K-means clustering algorithm is used to obtain a better prior bounding box, which is input into the YOLOv4 network to improve the positioning accuracy of the water column; finally, the convolutional block attention module (CBAM) is added in the PANet structure to improve the detection accuracy of the water column. The experimental results show that the above algorithm can effectively improve the detection accuracy and positioning accuracy of the water column at the impact point