Search Results (6)

It is important to maintain the safety of road driving by automatically performing a series of processes to automatically measure and repair damage to the road pavement. However, road pavements include not only damages such as longitudinal cracks, transverse cracks, alligator cracks, and potholes, but also various elements such as manholes, road marks, oil marks, shadows, and joints. Therefore, in order to separate categories that exist in various road pavements, in this paper, 13,500 digital, IR, and MSX images were collected and nine categories were automatically classified by DarkNet. The DarkNet classification accuracies of digital images, IR images, and MSX images are 97.4%, 80.1%, and 91.1%, respectively. The MSX image is a enhanced image of the IR image and showed an average of 6% lower accuracy than the digital image but an average of 11% higher accuracy than the IR image. Therefore, MSX images can play a complementary role if DarkNet classification is performed together with digital images. In this paper, a method for detecting the directionality of each crack through a two-dimensional wavelet transform is presented, and this result can contribute to future research on detecting cracks in pavements. Full article

Small space targets are usually present in the form of point sources when observed by space-based sensors. To ease the difficulty of obtaining real observation images and overcome the limitations of the existing Systems Tool Kit/electro-optical and infrared sensors (STK/EOIR) module in supporting the display and output of point target observation results from multiple platforms of the constellation, a method is provided for the fast simulation of point target groups using EOIR combined with external computation. A star lookup table based on the Midcourse Space Experiment (MSX) infrared astrometry catalog is established by dividing the grid to generate the background. A Component Object Model (COM) is used to connect STK to enable the rapid deployment and visualization of complex simulation scenarios. Finally, the automated output of simulated images and infrared information is achieved. Simulation experiments on point targets show that the method can support 20 sensors to image groups of targets at 128 × 128 resolution and achieve 32 frames of real-time output at 1 K × 1 K resolution, providing an effective approach to spatial situational awareness and the building of target infrared datasets. Full article

(1) Background: Hypodontia has a multifactorial aetiology, in which genetic factors are a major component. Associated with this congenital absence, the formed teeth may show differences in size and shape, which may vary with the specific genetic variants and with the location of the missing teeth. The aims of the present study were to investigate a specific variant of MSX1, derive morphometric tooth measurements in a sample of patients with isolated maxillary lateral incisor agenesis and matched controls, and model the findings. (2) Methods: Genotyping of the MSX1 rs8670 genetic variant and morphometric measurements with a 2D image analysis method were performed for 26 hypodontia patients and 26 matched controls. (3) Results: The risk of upper lateral incisor agenesis was 6.9 times higher when the T allele was present. The morphometric parameters showed significant differences between hypodontia patients and controls and between the unilateral and bilateral agenesis cases. The most affected crown dimension in the hypodontia patients was the bucco-lingual dimension. In crown shape there was significant variation the Carabelli trait in upper first molars. (4) Conclusions: The MSX1 rs8670 variant was associated with variations in morphological outcomes. The new findings for compensatory interactions between the maxillary incisors indicate that epigenetic and environmental factors interact with this genetic variant. A single-level directional complex interactive network model incorporates the variations seen in this study. Full article

Deep learning techniques underpinned by extensive data sources encompassing complex pavement features have proven effective in early pavement damage detection. With pavement features exhibiting temperature variation, inexpensive infra-red imaging technology in combination with deep learning techniques can detect pavement damages effectively. Previous experiments based on pavement data captured during summer sunny conditions when subjected to SA-ResNet deep learning architecture technique demonstrated 96.47% prediction accuracy. This paper has extended the same deep learning approach to a different dataset comprised of images captured during winter sunny conditions to compare the prediction accuracy, sensitivity and recall score with summer conditions. The results suggest that irrespective of the prevalent weather season, the proposed deep learning algorithm categorises pavement features around 92% accurately (95.18% in summer and 91.67% in winter conditions), suggesting the beneficial replacement of one image type with other. The data captured in sunny conditions during summer and winter show prediction accuracies of DC = 96.47% > MSX = 95.24% > IR-T = 93.83% and DC = 94.14% > MSX = 90.69% > IR-T = 90.173%, respectively. DC images demonstrated a sensitivity of 96.47% and 94.20% for summer and winter conditions, respectively, to demonstrate that reliable categorisation is possible with deep learning techniques irrespective of the weather season. However, summer conditions showing better overall prediction accuracy than winter conditions suggests that inexpensive IR-T imaging cameras with medium resolution levels can still be an economical solution, unlike expensive alternate options, but their usage has to be limited to summer sunny conditions. Full article

Automatic damage detection using deep learning warrants an extensive data source that captures complex pavement conditions. This paper proposes a thermal-RGB fusion image-based pavement damage detection model, wherein the fused RGB-thermal image is formed through multi-source sensor information to achieve fast and accurate defect detection including complex pavement conditions. The proposed method uses pre-trained EfficientNet B4 as the backbone architecture and generates an argument dataset (containing non-uniform illumination, camera noise, and scales of thermal images too) to achieve high pavement damage detection accuracy. This paper tests separately the performance of different input data (RGB, thermal, MSX, and fused image) to test the influence of input data and network on the detection results. The results proved that the fused image’s damage detection accuracy can be as high as 98.34% and by using the dataset after augmentation, the detection model deems to be more stable to achieve 98.35% precision, 98.34% recall, and 98.34% F1-score. Full article

The ability to accurately recognize fruit on trees is a critical step in robotic harvesting. Many researchers have investigated a variety of image analysis methods based on different imaging technologies for fruit recognition. However, challenges still occur in the implementation of this goal due to various factors, especially variable light and proximal color background. In this study, images with fruit were acquired with a Forward Looking Infrared (FLIR) camera based on the Multi-Spectral Dynamic Imaging (MSX) technology. In view of its imaging mechanism, the optimal timing and shooting angle for image acquisition were pre-analyzed to obtain the maximum contrast between fruit and background. An effective algorithm was developed for locking potential fruit regions, which was based on the pseudo-color and texture information from MSX images. The algorithm was applied to 506 training and 340 evaluating images, including a variety of fruit and complex backgrounds. Recognition precision and sensitivity of these complete fruit regions were both above 92%, and those of incomplete fruit regions were not lower than 72%. The average processing time for each image was less than 1 s. The results indicated that the developed algorithm based on MSX imaging was effective for fruit recognition and could be suggested as a potential method for the automation of orchard production. Full article