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Insect pest is an essential factor affecting crop yield, and the effect of pest control depends on the timeliness and accuracy of pest forecasting. The traditional method forecasts pest outbreaks by manually observing (capturing), identifying, and counting insects, which is very time-consuming and laborious. Therefore, developing a method that can more timely and accurately identify insects and obtain insect information. This study designed an image acquisition device that can quickly collect real-time photos of phototactic insects. A pest identification model was established based on a deep learning algorithm. In addition, a model update strategy and a pest outbreak warning method based on the identification results were proposed. Insect images were processed to establish the identification model by removing the background; a laboratory image collection test verified the feasibility. The results showed that the proportion of images with the background completely removed was 90.2%. Dataset 1 was obtained using reared target insects, and the identification accuracy of the ResNet V2 model on the test set was 96%. Furthermore, Dataset 2 was obtained in the cotton field using a designed field device. In exploring the model update strategy, firstly, the T_ResNet V2 model was trained with Dataset 2 using transfer learning based on the ResNet V2 model; its identification accuracy on the test set was 84.6%. Secondly, after reasonably mixing the indoor and field datasets, the SM_ResNet V2 model had an identification accuracy of 85.7%. The cotton pest image acquisition, transmission, and automatic identification system provide a good tool for accurately forecasting pest outbreaks in cotton fields.

The JUNGFRAU 4‐megapixel (4M) charge‐integrating pixel‐array detector, when operated at a full 2 kHz frame rate, streams data at a rate of 17 GB s−1. To operate this detector for macromolecular crystallography beamlines, a data‐acquisition system called Jungfraujoch was developed. The system, running on a single server with field‐programmable gate arrays and general‐purpose graphics processing units, is capable of handling data produced by the JUNGFRAU 4M detector, including conversion of raw pixel readout to photon counts, compression and on‐the‐fly spot finding. It was also demonstrated that 30 GB s−1 can be handled in performance tests, indicating that the operation of even larger and faster detectors will be achievable in the future. The source code is available from a public repository. A new data acquisition and real‐time image analysis system with FPGAs and GPUs for kilohertz macromolecular crystallography applications is presented.

Three-dimensional (3D) imaging technologies are increasingly used in craniomaxillofacial (CMF) surgery, especially to enable clinicians to get an effective approach and obtain better treatment results during different preoperative and postoperative phases, namely during image acquisition and diagnosis, virtual surgical planning (VSP), actual surgery, and treatment outcome assessment. The article presents an overview of 3D imaging technologies used in the aforementioned phases of the most common CMF surgery. We searched for relevant studies on 3D imaging applications in CMF surgery published over the past 10 years in the PubMed, ProQuest (Medline), Web of Science, Science Direct, Clinical Key, and Embase databases. A total of 2094 articles were found, of which 712 were relevant. An additional 26 manually searched articles were included in the analysis. The findings of the review demonstrated that 3D imaging technology is becoming increasingly popular in clinical practice and an essential tool for plastic surgeons. This review provides information that will help for researchers and clinicians consider the use of 3D imaging techniques in CMF surgery to improve the quality of surgical procedures and achieve satisfactory treatment outcomes.

During the tobacco leaf sorting process, manual factors can lead to non-compliant tobacco leaf loosening, resulting in low-quality tobacco leaf sorting such as mixed leaf parts, mixed grades, and contamination with non-tobacco related materials. Given the absence of established methodologies for monitoring and evaluating tobacco leaf sorting quality, this paper proposes a YOLO-TobaccoStem-based detection model for quantifying tobacco leaf loosening rates. Initially, a darkroom image acquisition system was constructed to create a stable monitoring environment. Subsequently, modifications were made to YOLOv8 to improve its multi-scale object detection capabilities. This was achieved by adding layers for detecting smaller objects and integrating a weighted bi-directional feature pyramid structure to reconstruct the feature fusion network. Additionally, a loss function with a monotonic focusing mechanism was introduced to increase the model’s learning capacity for difficult samples, resulting in a YOLO-TobaccoStem model more suitable for detecting tobacco stem objects. Lastly, a tobacco leaf loosening rate detection algorithm was formulated. The results from the YOLO-TobaccoStem were input into this algorithm to determine the compliance of the tobacco leaf loosening rate. The detection method achieved an F1-Score of 0.836 on the test set. Experimental results indicate that the proposed tobacco leaf loosening rate detection method has significant practical application value, enabling effective monitoring and evaluation of tobacco leaf sorting quality, thereby further enhancing the quality of tobacco leaf sorting.

The surface quality inspection of industrial printed circuit board (PCB) is a vitally important link in its manufacturing process. To inspect surface defects of PCBs effectively, the automatic optical inspection (AOI) technology, in which the PCB image acquisition depends on the path planning method, is widely adopted by industry. It is regarded as a characteristic travelling salesman problem (TSP), which includes component clustering, location adjustment and algorithm adaptation optimization. In this paper, by improving the ant colony algorithm (ACA) algorithm, we devise a PCB image acquisition path planning model and the corresponding solving algorithms. Because the ACA encounters difficulty escaping from the local optimal solution, an improved ACA with a negative feedback mechanism is proposed that is able to obtain a better tour path with a higher probability. Aiming at the uncertainty of the local location of image acquisition windows, location adjustment methods are introduced to further shorten the path length and improve the image acquisition efficiency. Finally, via simulation experiments, the proposed global negative feedback ACA (GNF-ACA) can shorten the average length of the tour path by 1.7% without changing the time complexity. The three methods of location adjustment can further shorten the length of the tour path by 5.6%, 13.1% and 13.7%.

The accuracy of river velocity measurements plays an important role in the effective management of water resources. Various methods have been developed to measure river velocity. Currently, image-based techniques provide a promising approach to avoid physical contact with targeted water bodies by researchers. In this study, measured surface velocities collected under low flow and high flow conditions in the Houlong River, Taiwan, using large-scale particle image velocimetry (LSPIV) captured by an unmanned aerial vehicle (UAV) and a terrestrial fixed station were analyzed and compared. Under low flow conditions, the mean absolute errors of the measured surface velocities using LSPIV from a UAV with shooting heights of 9, 12, and 15 m fell within 0.055 ± 0.015 m/s, which was lower than that obtained using LSPIV on video recorded from a terrestrial fixed station (i.e., 0.34 m/s). The mean absolute errors obtained using LSPIV derived from UAV aerial photography at a flight height of 12 m without seeding particles and with different seeding particle densities were slightly different, and fell within the range of 0.095 ± 0.025 m/s. Under high flow conditions, the mean absolute errors associated with using LSPIV derived from terrestrial fixed photography and LSPIV derived from a UAV with flight heights of 32, 62, and 112 m were 0.46 m/s and 0.49 m/s, 0.27 m, and 0.97 m/s, respectively. A UAV flight height of 62 m yielded the best measured surface velocity result. Moreover, we also demonstrated that the optimal appropriate interrogation area and image acquisition time interval using LSPIV with a UAV were 16 × 16 pixels and 1/8 s, respectively. These two parameters should be carefully adopted to accurately measure the surface velocity of rivers.

Ferrograph-based wear debris analysis (WDA) provides significant information for wear fault analysis of mechanical equipment. After decades of offline application, this conventional technology is being driven by the online ferrograph sensor for real-time wear state monitoring. However, online ferrography has been greatly limited by the low imaging quality and segmentation accuracy of particle chains when analyzing degraded lubricant oils in practical applications. To address this issue, an integrated optimization method is developed that focuses on two aspects: the structural re-design of the online ferrograph sensor and the intelligent segmentation of particle chains. For enhancing the imaging quality of wear particles, the magnetic pole of the online ferrograph sensor is optimized to enable the imaging system directly observe wear particles without penetrating oils. Furthermore, a light source simulation model is established based on the light intensity distribution theory, and the LED installation parameters are determined for particle illumination uniformity in the online ferrograph sensor. On this basis, a Mask-RCNN-based segmentation model of particle chains is constructed by specifically establishing the region of interest (ROI) generation layer and the ROI align layer for the irregular particle morphology. With these measures, a new online ferrograph sensor is designed to enhance the image acquisition and information extraction of wear particles. For verification, the developed sensor is tested to collect particle images from different degraded oils, and the images are further handled with the Mask-RCNN-based model for particle feature extraction. Experimental results reveal that the optimized online ferrography can capture clear particle images even in highly-degraded lubricant oils, and the illumination uniformity reaches 90% in its imaging field. Most importantly, the statistical accuracy of wear particles has been improved from 67.2% to 94.1%.

The purpose is to explore the effect of iris image acquisition and real-time detection systems based on Convolutional Neural Network (CNN) and improve the efficiency of iris real-time detection. Based on existing iris data acquisition and detection systems, this study uses the light field focusing algorithm to collect iris data in live, introduces CNN in Deep Learning (DL) algorithm, and designs an iris image acquisition and live detection system based on CNN. Afterward, Radial Basis Function (RBF)-Support Vector Machines (SVM) algorithm is used to classify iris feature information. Based on Field Programmable Gate Array (FPGA), a system for iris image acquisition, processing, and display is constructed. Finally, the performance of the constructed system and algorithm are analyzed through simulation experiments. The research results show that the proposed algorithm can automatically select the qualified iris images in live, significantly improve the recognition accuracy of the whole iris recognition system, and the average time of live quality evaluation for each frame image is less than 0.05 s. The focal point of the investigation involves the exploration of a CNN-based iris image acquisition and real-time detection system, with an emphasis on enhancing the efficiency of real-time iris detection. The innovation of this research lies in the integration of deep learning algorithms and light-field focusing techniques, applied to the reconstruction of a FPGA system. Further, the proposed algorithm is compared with Super-Resolution Using Very Deep Convolutional Networks (VDSR), Deeply Recursive Convolutional Network (DRCN), Residual Dense Network (RDN), and Bicubic. The comparison analysis suggests that the recognition accuracy of the proposed algorithm is the highest, close to 100%. Additionally, the proposed algorithm is compared with the Image Quality Evaluation-based Algorithm (IQA) and the Feature Extraction-based Algorithm (FEA), showing that the proposed RBF-SVM algorithm has higher classification accuracy (96.38%) and lower Average Classification Error Rate (ACER) (3.69%). The research results can provide a reference for live iris image detection and data acquisition.

High-resolution line scan cameras with wide-angle lenses are highly accurate and efficient for tunnel detection. However, due to the curvature of the tunnel, there are variations in object distance that exceed the depth of field of the lens, resulting in uneven defocus blur in the captured images. This can significantly affect the accuracy of defect recognition. While existing deblurring algorithms can improve image quality, they often prioritize results over inference time, which is not ideal for high-speed tunnel image acquisition. To address this issue, we developed a lightweight tunnel structure defect deblurring network (TSDDNet) for curved-tunnel line scanning with wide-angle lenses. Our method employs an innovative progressive structure that balances network depth and feature breadth to simultaneously achieve good performance and short inference time. The proposed depthwise ResBlocks significantly improves the parameter efficiency of the network. Additionally, the proposed feature refinement block captures the structurally similar features to enhance the image details, increasing the peak signal-to-noise ratio (PSNR). A raw dataset containing tunnel blur images was created using a high-resolution line scan camera and used to train and test our model. TSDDNet achieved a PSNR of 26.82 dB and a structural similarity index measure of 0.888, while using one-third of the parameters of comparable alternatives. Moreover, our method exhibited a higher computational speed than that of conventional methods, with inference times of 8.82 ms for a single 512 × 512 pixel image patch and 227.22 ms for completely processing a 2048 × 2560 pixel image. The test results indicated that the structural scalability of the network allows it to accommodate large inputs, making it effective for high-resolution images.

BackgroundMagnetic resonance (MR) imaging is the modality used for baseline assessment of locally advanced rectal cancer (LARC) and restaging after neoadjuvant treatment. The overall audited quality of MR imaging in large multicentre trials on rectal cancer is so far not routinely reported.Materials and methodsWe collected MR images obtained within the Rectal Cancer And Pre-operative Induction Therapy Followed by Dedicated Operation (RAPIDO) trial and performed an audit of the technical features of image acquisition. The required MR sequences and slice thickness stated in the RAPIDO protocol were used as a reference.ResultsOut of 920 participants of the RAPIDO study, MR investigations of 668 and 623 patients in the baseline and restaging setting, respectively, were collected. Of these, 304/668 (45.5%) and 328/623 (52.6%) MR images, respectively, fulfilled the technical quality criteria. The main reason for non-compliance was exceeding slice thickness 238/668, 35.6% in the baseline setting and 162/623, 26.0% in the restaging setting. In 166/668, 24.9% and 168/623, 27.0% MR images in the baseline and restaging setting, respectively, one or more of the required pulse sequences were missing.ConclusionAltogether, 49.0% of the MR images obtained within the RAPIDO trial fulfilled the image acquisition criteria required in the study protocol. High-quality MR imaging should be expected for the appropriate initial treatment and response evaluation of patients with LARC, and efforts should be made to maximise the quality of imaging in clinical trials and in clinical practice.Critical relevance statementThis audit highlights the importance of adherence to MR image acquisition criteria for rectal cancer, both in multicentre trials and in daily clinical practice. High-resolution images allow correct staging, treatment stratification and evaluation of response to neoadjuvant treatment.Key points- Complying to MR acquisition guidelines in multicentre trials is challenging.- Neglection on MR acquisition criteria leads to poor staging and treatment.- MR acquisition guidelines should be followed in trials and clinical practice.- Researchers should consider mandatory audits prior to study initiation.