Design, Fabrication, and Performance Evaluation of Portable and Large-Area Blackbody System.

In this study, a portable and large-area blackbody system was developed following a series of processes including design, computational analysis, fabrication, and experimental analysis and evaluation. The blackbody system was designed to be lightweight (5 kg), and its temperature could exceed the ambient temperature by up to 15 °C under operation. A carbon-fiber-based heat source was used to achieve a uniform temperature distribution. A heat shield fabricated from an insulation material was embedded at the opposite side of the heating element to minimize heat loss. A prototype of the blackbody system was fabricated based on the design and transient coupled electro-thermal simulation results. The operation performance of this system, such as the thermal response, signal transfer function, and noise equivalent temperature difference, was evaluated by employing an infrared imaging system. In addition, emissivity was measured during operation. The results of this study show that the developed portable and large-area blackbody system can be expected to serve as a reliable reference source for the calibration of aerial infrared images for the application of aerial infrared techniques to remote sensing.

Application of Deep-Learning Methods to Bird Detection Using Unmanned Aerial Vehicle Imagery.

Wild birds are monitored with the important objectives of identifying their habitats and estimating the size of their populations. Especially in the case of migratory bird, they are significantly recorded during specific periods of time to forecast any possible spread of animal disease such as avian influenza. This study led to the construction of deep-learning-based object-detection models with the aid of aerial photographs collected by an unmanned aerial vehicle (UAV). The dataset containing the aerial photographs includes diverse images of birds in various bird habitats and in the vicinity of lakes and on farmland. In addition, aerial images of bird decoys are captured to achieve various bird patterns and more accurate bird information. Bird detection models such as Faster Region-based Convolutional Neural Network (R-CNN), Region-based Fully Convolutional Network (R-FCN), Single Shot MultiBox Detector (SSD), Retinanet, and You Only Look Once (YOLO) were created and the performance of all models was estimated by comparing their computing speed and average precision. The test results show Faster R-CNN to be the most accurate and YOLO to be the fastest among the models. The combined results demonstrate that the use of deep-learning-based detection methods in combination with UAV aerial imagery is fairly suitable for bird detection in various environments.

3D Defect Localization on Exothermic Faults within Multi-Layered Structures Using Lock-In Thermography: An Experimental and Numerical Approach.

Micro-electronic devices are increasingly incorporating miniature multi-layered integrated architectures. However, the localization of faults in three-dimensional structure remains challenging. This study involved the experimental and numerical estimation of the depth of a thermally active heating source buried in multi-layered silicon wafer architecture by using both phase information from an infrared microscopy and finite element simulation. Infrared images were acquired and real-time processed by a lock-in method. It is well known that the lock-in method can increasingly improve detection performance by enhancing the spatial and thermal resolution of measurements. Operational principle of the lock-in method is discussed, and it is represented that phase shift of the thermal emission from a silicon wafer stacked heat source chip (SSHSC) specimen can provide good metrics for the depth of the heat source buried in SSHSCs. Depth was also estimated by analyzing the transient thermal responses using the coupled electro-thermal simulations. Furthermore, the effects of the volumetric heat source configuration mimicking the 3D through silicon via integration package were investigated. Both the infrared microscopic imaging with the lock-in method and FE simulation were potentially useful for 3D isolation of exothermic faults and their depth estimation for multi-layered structures, especially in packaged semiconductors.

CNN-based object detection and growth estimation of plum fruit (Prunus mume) using RGB and depth imaging techniques.

Modern people who value healthy eating habits have shown increasing interest in plum (Prunus mume) fruits, primarily owing to their nutritiousness and proven efficacy. As consumption increases, it becomes important to monitor work to prevent Prunus mume fruits from falling out. Moreover, determining the growth status of Prunus mume is also crucial and is attracting increasing attention. In this study, convolutional neural network (CNN)-based deep learning object detection was developed using RGBD images collected from Prunus mume farms. These RGBD images consider various environments, including the depth information of objects in the outdoor field. A faster region-based convolutional neural network (R-CNN), EfficientDet, Retinanet, and Single Shot Multibox Detector (SSD) were applied for detection, and the performance of all models was estimated by comparing their respective computing speeds and average precisions (APs). The test results show that the EfficientDet model is the most accurate, and SSD MobileNet is the fastest among the four models. In addition, the algorithm was developed to acquire the growth status of P. mume fruits by applying the coordinates and score values of bounding boxes to the depth map. Compared to the diameters of the artificial Prunus mume fruits used as the experimental group, the calculated diameters were very similar to those of the artificial objects. Collectively, the results demonstrate that the CNN-based deep learning Prunus mume detection and growth estimation method can be applied to real farmlands.

Automatic Pest Counting from Pheromone Trap Images Using Deep Learning Object Detectors for Matsucoccus thunbergianae Monitoring.

The black pine bast scale, M. thunbergianae, is a major insect pest of black pine and causes serious environmental and economic losses in forests. Therefore, it is essential to monitor the occurrence and population of M. thunbergianae, and a monitoring method using a pheromone trap is commonly employed. Because the counting of insects performed by humans in these pheromone traps is labor intensive and time consuming, this study proposes automated deep learning counting algorithms using pheromone trap images. The pheromone traps collected in the field were photographed in the laboratory, and the images were used for training, validation, and testing of the detection models. In addition, the image cropping method was applied for the successful detection of small objects in the image, considering the small size of M. thunbergianae in trap images. The detection and counting performance were evaluated and compared for a total of 16 models under eight model conditions and two cropping conditions, and a counting accuracy of 95% or more was shown in most models. This result shows that the artificial intelligence-based pest counting method proposed in this study is suitable for constant and accurate monitoring of insect pests.

Idiopathic synovial osteochondromatosis of the hip: radiographic and MR appearances in 15 patients.

OBJECTIVE: To evaluate the radiographic and MR appearance of idiopathic synovial osteochondromatosis of the hip. MATERIALS AND METHODS: Radiographs and MR images of 15 patients with idiopathic synovial osteochondromatosis of the hip were assessed. The former were analysed in terms of the presence of 1) juxta-articular calcified and/ or ossified bodies, 2) osteophytes, 3) bone erosion, 4) juxta-articular osteopenia, and 5) joint space narrowing, while for the latter, analysis focused on 1) the configuration of intra-articular bodies, 2) bone erosion, 3) synovial thickening, 4) conglomeration of intra-articular bodies, and 5) extra-articular extension. RESULTS: At hip radiography, juxta-articular calcified and/ or ossified bodies were seen in 12 of the 15 patients (80%), bone erosion in eight (53%), osteophytes in seven (47%), juxta-articular osteopenia in five (33%) and joint space narrowing in five (33%). In eight patients (53%), MR imaging depicted intra-articular bodies of focal low signal intensity at all pulse sequences, and areas of isointensity at T1WI and hyperintensity at T2WI. In three (20%), intra-articular bodies of focal low signal intensity and areas of hyperintensity at all pulse sequences were observed, with areas of iso-intensity at T1WI and hyperintensity at T2WI, while in four (27%), intra-articular bodies of only focal low signal intensity at all pulse sequences were apparent. Synovial thickening was present in 13 patients (87%), bone erosion in 11 (73%), conglomeration of the intra-articular bodies in 11 (73%), and an extra-articular herniation sac in six (40%). CONCLUSION: The most common radiographic finding of synovial osteochondromatosis of the hip was the presence of juxta-articular calcified and/ or ossified bodies. MR imaging depicted intra-articular bodies of focal low signal intensity at all pulse sequences, with areas of iso-intensity at T1WI and hyperintensity at T2WI. In addition, the presence of an extra-articular herniation sac was not uncommon.