Aiming at the problems of insufficient data volume and scarcity of target images in the target detection task of sonar images, this paper proposes an image style migration from optical to acoustic based on an improved cyclegan method based on the imaging principle and process of side-scan sonar images to realize the acoustic database augmentation so as to improve the status quo of the scarce number of samples of sonar images. In this paper, we enhance the feature extraction capability of the model network by introducing the cbam attention module to the original cyclegan generator, and evaluate the migration effect using the image quality assessment metrics is and fid. The experimental results conducted with the traditional method show that the method introducing the cbam attention mechanism module has a better style migration effect for sonar image generation.
KEYWORDS: Detection and tracking algorithms, Target detection, Object detection, Feature extraction, Deep learning, Education and training, Evolutionary algorithms, Small targets, Data modeling, Windows
The application of deep learning techniques significantly improves the performance of target detection algorithms. The article provides a comprehensive discussion of target detection principles, methods, algorithms, and future trends. First, the principles of target detection algorithms are summarized, including tasks, evaluation indexes, and public datasets. The image-based target detection algorithms are divided into two levels: traditional target detection and deep learning-based target detection. A brief overview of the types and shortcomings of traditional target detection algorithms is provided. Deep learning-based target detection is divided into single-stage and dual-stage for specific description. It summarizes the mechanisms, advantages, and limitations of each type of algorithm, conducts a comparative analysis, and suggests the future research direction of deep learning-based target detection.
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