Evolutionary computation-based self-supervised learning for image processing: a big data-driven approach to feature extraction and fusion for multispectral object detection

• Published in: Journal of big data Vol. 11; no. 1; pp. 130 - 20
• Main Authors: Shen, Xiaoyang, Li, Haibin, Shankar, Achyut, Viriyasitavat, Wattana, Chamola, Vinay
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2024; Springer Nature B.V; SpringerOpen
• Subjects: Artificial intelligence; Big Data; Communications Engineering; Computational Science and Engineering; Computer Science; Data integration; Data Mining and Knowledge Discovery; Database Management; Deep learning; Evolutionary algorithms; Evolutionary computation; Feature extraction; Feature recognition; Image enhancement; Image processing; Information Storage and Retrieval; Infrared imagery; Machine learning; Mathematical Applications in Computer Science; Networks; Object detection; Object recognition; Optimization; Parameter identification; Self-supervised learning; Target detection; Big data; Image processing; Evolutionary computation; Self-supervised learning; Object detection
• ISSN: 2196-1115; 2196-1115
• DOI: 10.1186/s40537-024-00988-5

The image object recognition and detection technology are widely used in many scenarios. In recent years, big data has become increasingly abundant, and big data-driven artificial intelligence models have attracted more and more attention. Evolutionary computation has also provided a powerful driving force for the optimization and improvement of deep learning models. In this paper, we propose an image object detection method based on self-supervised and data-driven learning. Differ from other methods, our approach stands out due to its innovative use of multispectral data fusion and evolutionary computation for model optimization. Specifically, our method uniquely combines visible light images and infrared images to detect and identify image targets. Firstly, we utilize a self-supervised learning method and the AutoEncoder model to perform high-dimensional feature extraction on the two types of images. Secondly, we fuse the extracted features from the visible light and infrared images to detect and identify objects. Thirdly, we introduce a model parameter optimization method using evolutionary learning algorithms to enhance model performance. Validation on public datasets shows that our method achieves comparable or superior performance to existing methods.