Method research on ship detection in remote sensing image based on Yolo algorithm

Small target recognition is a classic challenge in the field of target recognition. In this paper, an improved algorithm based on Yolo V3 is proposed for ship detection in remote sensing images. In order to solve the problem that the input satellite image size is too large, the window sliding segmentation technology is used to cut the large image into several small images; because the detection target only has a single kind of ship, K-means algorithm is used to re cluster anchor Box, to improve the detection accuracy of the algorithm; in view of the small ship target in the ocean background, the multi-level feature extraction method is used to highlight the feature vector of small target to improve the recognition ability of small target. Through the above-mentioned methods, using the data set of Airbus ship detection provided by kaggle, the ship target detection has achieved a better comprehensive accuracy rate, which is higher than the accuracy and recall rate of similar target detection algorithms.