Se Hyun Nam

Despite the rapid expansion of EVs (Electric Vehicles), current EV charging systems remain inconvenient for users. To address this issue, robot-assisted EV charging systems are emerging as promising solutions, requiring precise EV inlet localization as a key technology. However, existing vision-based methods face fundamental trade-offs: 2D RGB provides dense visual information but suffers from illumination sensitivity and depth ambiguity, while 3D PC (Point Cloud) provides geometric precision with limitations in sparse structures and high computational complexity. To overcome these challenges, we propose MDSNet (Multimodal Dual Stream Network), a novel framework that combines 2D and 3D CNNs through two key innovations: (1) modality-specific axial attention mechanisms for efficient feature fusion, and (2) hierarchical data slicing, tailored to each modality-specific characteristic. Unlike single-modality approaches, MDSNet effectively leverages complementary strengths while mitigating individual weaknesses of both RGB and PC data. The proposed method is evaluated on RGB-PC data pairs acquired from realistic EV scenarios under various real-world environments including multiple distances, viewing angles, and illumination variations. Experimental results demonstrate state-of-the-art performance, establishing MDSNet as a practical solution for autonomous robot-assisted EV charging systems.

Facial-image-based age estimation is being increasingly used in various fields. Examples include statistical marketing analysis based on age-specific product preferences, medical applications such as beauty products and telemedicine, and age-based suspect tracking in intelligent surveillance camera systems. Masks are increasingly worn for hygiene, personal privacy concerns, and fashion. In particular, the acquisition of mask-occluded facial images has become more frequent due to the COVID-19 pandemic. These images cause a loss of important features and information for age estimation, which reduces the accuracy of age estimation. Existing de-occlusion studies have investigated masquerade masks that do not completely occlude the eyes, nose, and mouth; however, no studies have investigated the de-occlusion of masks that completely occlude the nose and mouth and its use for age estimation, which is the goal of this study. Accordingly, this study proposes a novel low-complexity attention-generative adversarial network (LCA-GAN) for facial age estimation that combines an attention architecture and conditional generative adversarial network (conditional GAN) to de-occlude mask-occluded human facial images. The open databases MORPH and PAL were used to conduct experiments. According to the results, the mean absolution error (MAE) of age estimation with the de-occluded facial images reconstructed using the proposed LCA-GAN is 6.64 and 6.12 years, respectively. Thus, the proposed method yielded higher age estimation accuracy than when using occluded images or images reconstructed using the state-of-the-art method.

Few studies have been conducted on thermal plant images. This is because of the difficulty in extracting and analyzing various color-related patterns and features from the plant image obtained using a thermal camera, which does not provide color information. In addition, the thermal camera is sensitive to the surrounding temperature and humidity. However, the thermal camera enables the extraction of invisible patterns in the plant by providing external and internal heat information. Therefore, this study proposed a novel plant classification method based on both the thermal and visible-light plant images to exploit the strengths of both types of cameras. To the best of our knowledge, this study is the first to perform super-resolution reconstruction using visible-light and thermal plant images. Furthermore, a method to improve the classification performance through generative adversarial network (GAN)-based super-resolution reconstruction was proposed. Through the experiments using a self-collected dataset of thermal and visible-light images, our method shows higher accuracies than the state-of-the-art methods.

Computer-aided diagnosis (CAD) systems have been used to assist doctors (radiologists) in diagnosing many types of diseases, such as thyroid, brain, breast, and lung cancers. Previous studies have successfully built CAD systems using large, annotated datasets to train their models. The use of a large volume of training data helps these CAD systems to collect rich information for application in the diagnosis process. However, a large amount of training data is sometimes unavailable for training the models, such as for a new or less common disease and diseases that require expensive image acquisition devices. In such cases, conventional CAD systems are unable to learn their models efficiently. As a result, diagnostic performance is reduced. In this study, we focus on dealing with this problem; thus, our classification method can enhance the performance of conventional CAD systems based on the ensemble model of a support vector machine (SVM), multilayer perceptron (MLP), and few-shot (FS) learning network when working with small training datasets of brain tumor images. Through experiments, we confirmed that our proposed method outperforms conventional deep learning-based CAD systems when working with a small training dataset. In detail, we verified that the lack of training data led to the reduction of classification performance. In addition, we enhanced the classification accuracy from 3% to 10% compared to previous studies that used the SVM-based classification method or fine-tuning of a convolutional neural network (CNN) using two public datasets.

There have been various studies conducted on plant images. Machine learning algorithms are usually used in visible light image-based studies, whereas, in thermal image-based studies, acquired thermal images tend to be analyzed with a naked eye visual examination. However, visible light cameras are sensitive to light, and cannot be used in environments with low illumination. Although thermal cameras are not susceptible to these drawbacks, they are sensitive to atmospheric temperature and humidity. Moreover, in previous thermal camera-based studies, time-consuming manual analyses were performed. Therefore, in this study, we conducted a novel study by simultaneously using thermal images and corresponding visible light images of plants to solve these problems. The proposed network extracted features from each thermal image and corresponding visible light image of plants through residual block-based branch networks, and combined the features to increase the accuracy of the multiclass classification. Additionally, a new database was built in this study by acquiring thermal images and corresponding visible light images of various plants.

Biometrics is a method of recognizing a person based on one or more unique physical and behavioral characteristics. Since each person has a different structure and shape, it is highly secure and more convenient than the existing security system. Among various biometric authentication methods, finger-vein recognition has advantages in that it is difficult to forge because a finger-vein exists inside one’s finger and high user convenience because it uses a non-invasive device. However, motion and optical blur may occur for some reasons such as finger movement and camera defocusing during finger-vein recognition, and such blurring occurrences may increase finger-vein recognition error. However, there has been no research on finger-vein recognition considering both motion and optical blur. Therefore, in this study, we propose a new method for increasing finger-vein recognition accuracy based on a network for the restoration of motion and optical blurring in a finger-vein image (RMOBF-Net). Our proposed network continuously maintains features that can be utilized during motion and optical blur restoration by actively using residual blocks and feature concatenation. Also, the architecture RMOBF-Net is optimized to the finger-vein image domain. Experimental results are based on two open datasets, the Shandong University homologous multi-modal traits finger-vein database and the Hong Kong Polytechnic University finger-image database version 1, from which equal error rates of finger-vein recognition accuracy of 4.290–5.779% and 2.465–6.663% were obtained, respectively. Higher performance was obtained from the proposed method compared with that of state-of-the-art methods.

Extensive research has been conducted on image augmentation, segmentation, detection, and classification based on plant images. Specifically, previous studies on plant image classification have used various plant datasets (fruits, vegetables, flowers, trees, etc., and their leaves). However, existing plant-based image datasets are generally small. Furthermore, there are limitations in the construction of large-scale datasets. Consequently, previous research on plant classification using small training datasets encountered difficulties in achieving high accuracy. However, research on plant image classification based on small training datasets is insufficient. Accordingly, this study performed classification by reducing the number of training images of plant-image datasets by 70%, 50%, 30%, and 10%, respectively. Then, the number of images was increased back through augmentation methods for training. This ultimately improved the plant-image classification performance. Based on the respective preliminary experimental results, this study proposed a plant-image classification convolutional neural network (PI-CNN) based on plant image augmentation using a plant-image generative adversarial network (PI-GAN). Our proposed method showed the higher classification accuracies compared to the state-of-the-art methods when the experiments were conducted using four open datasets of PlantVillage, PlantDoc, Fruits-360, and Plants.

Because of the limitations of previous studies on a fall detection system (FDS) based on wearable and ambient devices and visible light and depth cameras, the research using thermal cameras has recently been conducted. However, they also have the problem of deteriorating the accuracy of FDS depending on various environmental changes. Given these facts, in this study, we newly propose an FDS method based on the squeeze and excitation (SE) 3D convolutional neural networks (S3D). In our method, keyframes are extracted from input thermal videos using the optical flow vectors, and the fall detection is carried out based on the output of the proposed S3D, using the extracted keyframes as input. Comparative experiments were carried out on three open databases of thermal videos with different image resolutions, and our proposed method obtained F1 scores of 97.14%, 95.30%, and 98.89% in the Thermal Simulated Fall, Telerobotics and Control Lab fall detection, and eHomeSeniors datasets, respectively (the F1 score is a harmonic mean of recall and precision; it was confirmed that these are superior results to those obtained using the state-of-the-art methods of a thermal camera-based FDS.

Age estimation is applicable in various fields, and among them, research on age estimation using human facial images, which are the easiest to acquire, is being actively conducted. Since the emergence of deep learning, studies on age estimation using various types of convolutional neural networks (CNN) have been conducted, and they have resulted in good performances, as clear images with high illumination were typically used in these studies. However, human facial images are typically captured in low-light environments. Age information can be lost in facial images captured in low-illumination environments, where noise and blur generated by the camera in the captured image reduce the age estimation performance. No study has yet been conducted on age estimation using facial images captured under low light. In order to overcome this problem, this study proposes a new generative adversarial network for low-light age estimation (LAE-GAN), which compensates for the brightness of human facial images captured in low-light environments, and a CNN-based age estimation method in which compensated images are input. When the experiment was conducted using the MORPH, AFAD, and FG-NET databases—which are open databases—the proposed method exhibited more accurate age estimation performance and brightness compensation in low-light images compared to state-of-the-art methods.

The datasets used in recent age estimation studies largely consist of two races (i.e., Asians or Westerners), and despite the large amount of data available, the problems regarding age–class imbalances still arise, owing to different age distributions. This causes overfitting in training process, reducing the generality of the age estimation. This problem typically occurs in homogeneous datasets, e.g., using the same Asian database for training and testing or using a database with the same age range for training and testing. Consequently, the problems arise in heterogeneous datasets, e.g., using an Asian database in training and a Westerner database in testing or using databases of different age ranges in training and testing, and the accuracy inevitably degrades when heterogeneous datasets are used for training and testing. To solve these problems, we proposes an enhanced cycle generative adversarial network (CycleGAN)-based heterogeneous race and age image transformation technique, which can transform the images of one race and age range to those of different race and age range. The encoder and decoder of the generator in the proposed enhanced CycleGAN include residual connections, thereby preventing information loss as much as possible as the layer deepens. In addition, the generator of the enhanced CycleGAN uses identity loss and age loss functions between the generator-produced image and a multi-channel input image obtained through 3D one-hot encoding. Through this, the training is directed to increasing the similarity not only between the images but also between the age class labels. And the enhanced CycleGAN uses a second discriminator in addition to the existing discriminator, thereby addressing a problem in which training is not properly performed when the discriminator converges too fast relative to the generator in a conventional CycleGAN. Experiments with three open databases demonstrated that our method outperforms state-of-the-art methods for facial image transformation and age estimation.