Byeong Hak Kim

This study investigates an inpainting-based data generation approach for urban change detection and analyzes its impact on the model generalization performance. The proposed method constructs temporally consistent before and after image pairs by applying inpainting to existing images, thereby enabling seamless integration with existing change detection frameworks. Rather than replacing real-world datasets, the generated data were designed to increase the training diversity and mitigate dataset-specific biases. Comprehensive experiments were conducted using multiple representative change detection architectures across different datasets. The results show that incorporating the generated data alongside real training samples improves the cross dataset generalization performance, particularly in unseen target domains, whereas the degree of improvement varies depending on the model characteristics. By contrast, training with the generated data alone leads to limited generalization when evaluated on real test sets. These findings indicate that inpainting-based synthetic data can serve as a complementary training resource for enhancing the robustness of urban change detection. Future work will focus on reducing the domain gap between synthetic and real data, generating more diverse change scenarios, and further improving the reliability of data quality assessment.

This paper presents a real-time visual servoing system for automating die-centering in catheter extrusion processes. By leveraging state-of-the-art image processing algorithms, the system dynamically adjusts the positions of the extrusion head and tip in real time, ensuring precise alignment and significantly enhancing product consistency and quality. The proposed algorithm, a cornerstone of intelligent manufacturing, has been rigorously tested under diverse extrusion head types and operational conditions, demonstrating robust performance and precise detection of the die’s inner circle and the tip’s center position. In particular, the developed algorithm achieved a mean IoU (Intersection over Union) of 0.95, demonstrating exceptional robustness in inference performance even under dynamic conditions, such as the positional movements of the add-on system. The control signals generated by the algorithm were employed to align the tip and die, achieving precise operations at a scale of tens of micrometers, thereby validating the system’s high accuracy and reliability. Designed for seamless integration into existing manufacturing environments, the system requires minimal modifications, making it highly adaptable. By addressing long-standing challenges in die-centering operations, this intelligent manufacturing innovation not only increases production efficiency but also elevates product reliability.

This paper proposes a safety response technology that leverages multi-domain vision sensors to monitor the positions of workers and robots in real-time. The technology calculates flexible protective separation distances (FPSD), which facilitate dynamic control of robot velocity and enable safety stops. Compliant with the ISO/TS 15066 technical specifications for speed and separation monitoring, this method not only satisfies stringent safety standards but also promotes a more adaptable working environment and improves productivity. The proposed method has been analyzed and validated using data acquired from multi-domain sensors within the collaborative workspace of worker-robot collaboration tasks, and its operation has been confirmed at variable worker and robot velocities through experimental scenarios.

This paper proposes an algorithm that improves ship detection accuracy using preprocessing and post-processing. To achieve this, high-resolution electro-optical satellite images with a wide range of shape and texture information were considered. The developed algorithms display the problem of unreliable detection of ships owing to clouds, large waves, weather influences, and shadows from large terrains. False detections in land areas with image information similar to that of ships are observed frequently. Therefore, this study involves three algorithms: global feature enhancement pre-processing (GFEP), multiclass ship detector (MSD), and false detected ship exclusion by sea land segmentation image (FDSESI). First, GFEP enhances the image contrast of high-resolution electro-optical satellite images. Second, the MSD extracts many primary ship candidates. Third, falsely detected ships in the land region are excluded using the mask image that divides the sea and land. A series of experiments was performed using the proposed method on a database of 1984 images. The database includes five ship classes. Therefore, a method focused on improving the accuracy of various ships is proposed. The results show a mean average precision (mAP) improvement from 50.55% to 63.39% compared with other deep learning-based detection algorithms.

Advanced image sensors with high resolution are now being developed for specially purposed electro-optical systems, with research focused on robust image quality performance in terms of super resolution and noise removal under various environmental conditions. Recently, machine-learning and deep-learning methods have been studied as the best practical techniques for restoration to improve the deteriorated image quality of sensors. However, these methods show limitations and side effects of image degradation such as image non-uniformity. In this paper, we analyze and randomly generate additive white Gaussian noise, non-uniform line noise, and dark saturation as representative image degradations. We then propose an advanced U-net model based on global and local residual learning in order to restore complexly deteriorated images. The proposed method shows unparalleled performance compared to alternative models and previous studies. In particular, various complex noise components are minimized and improved with equal quality so that variation between sequential images is minimized. These findings leverage mutual corroboration of quantitative and qualitative evaluation metrics. In the future, the proposed model is expected to contribute to a wide range of field applications such as defense, surveillance, and video media for image quality enhancement technologies.

Although recently developed trackers have shown excellent performance even when tracking fast moving and shape changing objects with variable scale and orientation, the trackers for the electro-optical targeting systems (EOTS) still suffer from abrupt scene changes due to frequent and fast camera motions by pan-tilt motor control or dynamic distortions in field environments. Conventional context aware (CA) and deep learning based trackers have been studied to tackle these problems, but they have the drawbacks of not fully overcoming the problems and dealing with their computational burden. In this paper, a global motion aware method is proposed to address the fast camera motion issue. The proposed method consists of two modules: (i) a motion detection module, which is based on the change in image entropy value, and (ii) a background tracking module, used to track a set of features in consecutive images to find correspondences between them and estimate global camera movement. A series of experiments is conducted on thermal infrared images, and the results show that the proposed method can significantly improve the robustness of all trackers with a minimal computational overhead. We show that the proposed method can be easily integrated into any visual tracking framework and can be applied to improve the performance of EOTS applications.