Through the revelation and internal logic relationship of the DIKW model, the logic of data, information, knowledge, and wisdom in small target detection research initially built a new point of view, and did some basic tasks for small target detection and research, so as to use it to use it to do some basic tasks for small target detection and research. Small target detection research model under DIKW. The DIKW Information System was introduced into the field of small target testing. First, the DIKW model was analyzed. The framework of data information-information knowledge-intelligent framework was studied, and small target detection methods of one-stage and two-stage stages were studied, such as CNN-based methods, transformer methods and mixed methods. This article analyzes the basic network framework of small target detection, and builds data, information, knowledge and wisdom in small targets detection research.
This study underscores the significance of accurately identifying minute objects within satellite imagery. We propose an innovative GAN framework tailored for enhancing the detection of minute objects in such imagery. Our approach primarily emphasizes differentiating these objects from their surroundings by leveraging the core mechanisms of GANs. Moreover, we address the varied spectral characteristics of the background by integrating a similarity constraint into our GANs architecture. This strategy effectively distinguishes between the target object regions and non-object regions, significantly reducing the occurrence of false positives. Additionally, we employ a smoothness regularization technique to preserve the structural integrity of the detected objects. Through extensive experimentation on publicly available remote sensing datasets—spanning mineral, fabric, and vehicle detection—we compare our method quantitatively against conventional small object detection techniques and state-of-the-art GAN-based models. The comparative results consistently showcase the superior performance and robustness of our GAN-based approach in detecting minute objects within remote sensing imagery.
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