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In this paper, the paper cups were used as the research objects, and the machine vision detection technology was combined with different image processing techniques to investigate a non-contact optical automatic detection system to identify the defects of the manufactured paper cups. The combined ring light was used as the light source, an infrared (IR) LED matrix panel was used to provide the IR light to constantly highlight the outer edges of the detected objects, and a multi-grid pixel array was used as the image sensor. The image processing techniques, including the Gaussian filter, Sobel operator, Binarization process, and connected component, were used to enhance the inspection and recognition of the defects existing in the produced paper cups. There were three different detection processes for paper cups, which were divided into internal, external, and bottom image acquisition processes. The present study demonstrated that all the detection processes could clearly detect the surface defect features of the manufactured paper cups, such as dirt, burrs, holes, and uneven thickness. Our study also revealed that the average time for the investigated Automatic Optical Detection to detect the defects on the paper cups was only 0.3 s.

Accurate and efficient estimation of forest volume or biomass is critical for carbon cycles, forest management, and the timber industry. Individual tree detection and segmentation (ITDS) is the first and key step to ensure the accurate extraction of detailed forest structure parameters from LiDAR (light detection and ranging). However, ITDS is still a challenge to achieve using UAV-LiDAR (LiDAR from Unmanned Aerial Vehicles) in broadleaved forests due to the irregular and overlapped canopies. We developed an efficient and accurate ITDS framework for broadleaved forests based on UAV-LiDAR point clouds. It involves ITD (individual tree detection) from point clouds taken during the leaf-off season, initial ITS (individual tree segmentation) based on the seed points from ITD, and improvement of initial ITS through a refining process. The results indicate that this new proposed strategy efficiently provides accurate results for ITDS. We show the following: (1) point-cloud-based ITD methods, especially the Mean Shift, perform better for seed point selection than CHM-based (Canopy Height Model) ITD methods on the point clouds from leaf-off seasons; (2) seed points significantly improved the accuracy and efficiency of ITS algorithms; (3) the refining process using DBSCAN (density-based spatial clustering of applications with noise) and kNN (k-Nearest Neighbor classifier) classification significantly reduced edge errors in ITS results. Our study developed a novel ITDS strategy for UAV-LiDAR point clouds that demonstrates proficiency in dense deciduous broadleaved forests, and this proposed ITDS framework could be applied to single-phase point clouds instead of the multi-temporal LiDAR data in the future if the point clouds have detailed tree trunk points.

The identification of maritime targets plays a critical role in ensuring maritime safety and safeguarding against potential threats. While satellite remote-sensing imagery serves as the primary data source for monitoring maritime targets, it only provides positional and morphological characteristics without detailed identity information, presenting limitations as a sole data source. To address this issue, this paper proposes a method for enhancing maritime target identification and positioning accuracy through the fusion of Automatic Identification System (AIS) data and satellite remote-sensing imagery. The AIS utilizes radio communication to acquire multidimensional feature information describing targets, serving as an auxiliary data source to complement the limitations of image data and achieve maritime target identification. Additionally, the positional information provided by the AIS can serve as maritime control points to correct positioning errors and enhance accuracy. By utilizing data from the Jilin-1 Spectral-01 satellite imagery with a resolution of 5 m and AIS data, the feasibility of the proposed method is validated through experiments. Following preprocessing, maritime target fusion is achieved using a point-set matching algorithm based on positional features and a fuzzy comprehensive decision method incorporating attribute features. Subsequently, the successful fusion of target points is utilized for positioning error correction. Experimental results demonstrate a significant improvement in maritime target positioning accuracy compared to raw data, with over a 70% reduction in root mean square error and positioning errors controlled within 4 pixels, providing relatively accurate target positions that essentially meet practical requirements.

Potholes are the most common cause of accidents on the road surface, and the primary cause is water. Potholes in the pavement can be formed by a number of things, including gasoline or fuel leaks, automobile smearing, and the disposal of rock cuttings. As a result, in order to avoid accidents, it is essential to identify the pothole in advance, either automatically or manually. There are several ways to detect the pothole manually, nonetheless, they consume more time, power, and high setup cost. However, the automatic detection follows an optical imaging system, and the detection of pothole during bad weather conditions and night-time become arduous. Hence we proposed a novel method to detect the pothole by using a thermal imaging system known as convolutional neural network (CNN)-based modified aquilla optimization (AO) algorithm. The proposed method follows Data acquisition, Image preprocessing, and Data augmentation processes prior to the application of classification tasks. The proposed CNN-based Modified AO approach enhances the classification accuracy, precision, recall, and F1-score. However, it minimizes the classification error and detection time. The performances of our proposed work are compared with other approaches such as CNN, CNN-TI, YOLO-NN, and DNN. The experimental analysis also depicts that our proposed work has better performances than the other approaches.

Free-space optical (FSO) communications represent an attractive technology for future high-capacity wireless and satellite networks, offering multi-Gbps data rates, unlicensed spectrum, and built-in physical-layer security. However, their performance is severely affected by atmospheric turbulence, misalignment errors, and noise, which limit reliability and throughput. Hybrid automatic repeat request (HARQ) protocols provide a powerful mechanism to mitigate such impairments by combining forward error correction with retransmissions. In this paper, we investigate the fundamental performance limits of HARQ applied to FSO systems employing On–Off Keying (OOK) modulation. Using information-theoretic tools, we characterize the achievable rate and the finite-blocklength performance by resorting to channel dispersion, which plays a crucial role in quantifying rate–reliability tradeoffs. We further examine the interaction between HARQ retransmissions, turbulence-induced fading, and feedback delay, providing insights into the design of low-latency, high-reliability optical links. This analysis highlights how HARQ improves the robustness of OOK-based FSO systems and provides guidelines for parameter selection in next-generation space and terrestrial optical networks.

Occupancy grid maps are widely used as an environment model that allows the fusion of different range sensor technologies in real-time for robotics applications. In an autonomous vehicle setting, occupancy grid maps are especially useful for their ability to accurately represent the position of surrounding obstacles while being robust to discrepancies between the fused sensors through the use of occupancy probabilities representing uncertainty. In this article, we propose to evaluate the applicability of real-time vehicle detection on occupancy grid maps. State of the art detectors in sensor-specific domains such as YOLOv2/YOLOv3 for images or PIXOR for LiDAR point clouds are modified to use occupancy grid maps as input and produce oriented bounding boxes enclosing vehicles as output. The five proposed detectors are trained on the Waymo Open automotive dataset and compared regarding the quality of their detections measured in terms of Average Precision (AP) and their real-time capabilities measured in Frames Per Second (FPS). Of the five detectors presented, one inspired from the PIXOR backbone reaches the highest AP0.7 of 0.82 and runs at 20 FPS. Comparatively, two other proposed detectors inspired from YOLOv2 achieve an almost as good, with a AP0.7 of 0.79 while running at 91 FPS. These results validate the feasibility of real-time vehicle detection on occupancy grids.

Precision steel balls are among the most crucial components in the industry, widely used in various equipment related to bearings, such as CNC, automotive, medical, and machinery industries. Due to the reflective surface of steel balls, flaw inspection becomes a challenging task. This paper introduces an automatic optical inspection system that employs a modified DRAEM, a reconstruction-based anomaly detection network, for examining the surface of precision steel balls. We made three modifications to the DRAEM network (Zavrtanik, V., Kristan, M., & Skoca, D. (2021). DRAEM—a discriminatively trained reconstruction embedding for surface anomaly detection. http://arXiv.org/arXiv:2108.07610[cs.CV]), including adjusting the generation process of synthesized anomalies, adding a few skip connections from the encoder to the decoder, and incorporating an attention module to enhance the quality of reconstructed images and reduce misjudgments. Experimental results demonstrate a reduction in the model's underkill rate from 8.8% to 4.6% and the overkill rate from 1.5% to 0.4%. This indicates that the proposed methods addressed the issues of reconstruction distortion and the inability to detect small and inconspicuous defects. The proposed system has been successfully implemented in a case study company, showcasing significant advantages, particularly in scenarios involving new production lines or a lack of sufficient defective samples for collection.

Detecting changes in buildings over time is an important issue in monitoring urban areas, landscape changes, assessing natural disaster risks or updating geospatial databases. Three-dimensional (3D) information derived from dense image matching or laser data can effectively extract changes in buildings. This research proposes an automated method for detecting building changes in urban areas using archival aerial images and LiDAR data. The archival images, dating from 1970 to 1993, were subjected to a dense matching procedure to obtain point clouds. The LiDAR data came from 2006 and 2012. The proposed algorithm is based on height difference-generated nDSM. In addition, morphological filters and criteria considering area size and shape parameters were included. The study was divided into two sections: one concerned the detection of buildings from LiDAR data, an issue that is now widely known and used; the other concerned an attempt at automatic detection from archived aerial images. The automation of detection from archival data proved to be complex, so issues related to the generation of a dense point cloud from this type of data were discussed in detail. The study revealed problems of archival images related to the poor identification of ground control points (GCP), insufficient overlap between images or poor radiometric quality of the scanned material. The research showed that over the 50 years, the built-up area increased as many as three times in the analysed area. The developed method of detecting buildings calculated at a level of more than 90% in the case of the LiDAR data and 88% based on the archival data.

Intelligent robotic welding requires automatic finding of the seam geometrical features in order for an efficient intelligent control. Performance of the system, therefore, heavily depends on the success of the seam finding stage. Among various seam finding techniques, active laser vision is the most effective approach. It typically requires high-quality lasers, camera and optical filters. The success of the algorithm is highly sensitive to the image processing and feature extraction algorithms. In this work, sequential image processing and feature extraction algorithms are proposed to effectively extract the seam geometrical properties from a low-quality laser image captured without the conventional narrow band filter. A novel method of laser segmentation and detection is proposed. The segmentation method involves averaging, colour processing and blob analysis. The detection method is based on a novel median filtering technique that involves enhancing of the image object based on its underlying structure and orientation in the image. The method when applied enhances the vertically oriented laser stripe in the image which improves the laser peak detection. The image processing steps are performed to make sure that the laser profile is accurately extracted within the region of interest (ROI). Feature extraction algorithm based on pixels’ intensity distribution and neighbourhood search is also proposed that can effectively extract the seam feature points. The proposed algorithms have been implemented and evaluated on various background complexities, seam sizes, material type and laser types before and during the welding operation.

A near-real-time automatic detection system, based on the synergy of continuous measurements taken by a ceilometer and a photometer, has been implemented in order to detect lofted atmospheric aerosol layers and estimate the aerosol load. When heavy-loaded conditions are detected (defined by a significant deviation of the optical properties from a 10-year climatology), obtained for aerosol layers above 2500 m, an automatic alert is sent to scientists of the Romanian Lidar Network (ROLINET) to further monitor the event. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) back-trajectory calculations are used to establish the possible pollution source. The aerosol transport events are considered to be major when various optical properties provided by the photometer are found outside the climatological values. The aerosol types over the three years for all the events identified revealed that the contribution to the pollution was 31%, 9%, and 60% from marine, dust, and continental types. Considering only the ‘outside climatology limits’ events, the respective contribution was 15%, 12%, and 73% for marine, dust, and continental types, respectively.