Public safety is a critical concern, typically addressed through security checks at entrances of public places, involving trained officers or X-ray scanning machines to detect prohibited items. However, many places like hospitals, schools, and event centres lack such resources, risking security breaches. Even with X-ray scanners or manual checks, gaps can be exploited by individuals with malicious intent, posing significant security risks. Additionally, traditional methods, relying on manual inspections and conventional image processing techniques, are often inefficient and prone to high error rates. To mitigate these risks, we propose a real-time detection model - EnhanceNet using a customized Scale-Enhanced Pooling Network (SEP-Net) integrated into the YOLOv4. The innovative SEP-Net enhances feature representation and localization accuracy, significantly contributing to the model\'s efficacy in detecting prohibited items. We annotated a custom dataset of nine classes and evaluated our models using different input sizes (608 and 416). The 608 input size achieved a mean Average Precision (mAP) of 74.10% with a detection speed of 22.3 Frames per Second (FPS). The 416 input size showed superior performance, achieving a mAP of 76.75% and a detection speed of 27.1 FPS. These demonstrate that our models are accurate and efficient, making them suitable for real-time applications.

UNASSIGNED: Lung image segmentation plays an important role in computer-aid pulmonary disease diagnosis and treatment.
UNASSIGNED: This paper explores the lung CT image segmentation method by generative adversarial networks. We employ a variety of generative adversarial networks and used their capability of image translation to perform image segmentation. The generative adversarial network is employed to translate the original lung image into the segmented image.
UNASSIGNED: The generative adversarial networks-based segmentation method is tested on real lung image data set. Experimental results show that the proposed method outperforms the state-of-the-art method.
UNASSIGNED: The generative adversarial networks-based method is effective for lung image segmentation.

To address the issue of automated apple harvesting in orchards, we propose a YOLOv5-RACF algorithm for identifying apples and calculating apple diameters. This algorithm employs the robot operating dystem (ROS) to control the robot\'s locomotion system, Lidar mapping, and navigation, as well as the robotic arm\'s posture and grasping operations, achieving automated apple harvesting and placement. The tests were conducted in an actual orchard environment. The algorithm model achieved an average apple detection accuracy (mAP@0.5) of 98.748% and a (mAP@0.5:0.95) of 90.02%. The time to calculate the diameter of one apple was 0.13 s, with a measurement accuracy within an error range of 1-3 mm. The robot takes an average of 9 s to pick an apple and return to the initial pose. These results demonstrate the system\'s efficiency and reliability in real agricultural environments.

To enhance the synchronous detection of the horizontal and vertical positions of the torch in swing arc narrow gap welding, a torch pose detection (TPD) method is proposed. This approach utilizes passive visual sensing to capture images of the arc on the groove sidewall, using advanced image processing methods to extract and fit the arc contour. The coordinates of the arc contour center point and the highest point are determined through the arc contour fitting line. The torch center position is calculated from the average horizontal coordinates of the arc contour centers in adjacent welding images, while the height position is determined from the vertical coordinate of the arc\'s highest point. Experimental validation in both variable and constant groove welding conditions demonstrated the TPD method\'s accuracy within 0.32 mm for detecting the torch center position. This method eliminates the need to construct the wire centerline, which was a requirement in previous approaches, thereby reducing the impact of wire straightness on detection accuracy. The proposed TPD method successfully achieves simultaneous detection of the torch center and height positions, laying the foundation for intelligent detection and adaptive control in swing arc narrow gap welding.

Autism spectrum disorder (ASD) is a complex psychological syndrome characterized by persistent difficulties in social interaction, restricted behaviours, speech, and nonverbal communication. The impacts of this disorder and the severity of symptoms vary from person to person. In most cases, symptoms of ASD appear at the age of 2 to 5 and continue throughout adolescence and into adulthood. While this disorder cannot be cured completely, studies have shown that early detection of this syndrome can assist in maintaining the behavioural and psychological development of children. Experts are currently studying various machine learning methods, particularly convolutional neural networks, to expedite the screening process. Convolutional neural networks are considered promising frameworks for the diagnosis of ASD. This study employs different pre-trained convolutional neural networks such as ResNet34, ResNet50, AlexNet, MobileNetV2, VGG16, and VGG19 to diagnose ASD and compared their performance. Transfer learning was applied to every model included in the study to achieve higher results than the initial models. The proposed ResNet50 model achieved the highest accuracy, 92%, compared to other transfer learning models. The proposed method also outperformed the state-of-the-art models in terms of accuracy and computational cost.

The deflection modeling during the insertion of bevel-tipped flexible needles into soft tissues is crucial for robot-assisted flexible needle insertion into specific target locations within the human body during percutaneous biopsy surgery. This paper proposes a mechanical model based on cutting force identification to predict the deflection of flexible needles in soft tissues. Unlike other models, this method does not require measuring Young\'s modulus (E) and Poisson\'s ratio (ν) of tissues, which require complex hardware to obtain. In the model, the needle puncture process is discretized into a series of uniform-depth puncture steps. The needle is simplified as a cantilever beam supported by a series of virtual springs, and the influence of tissue stiffness on needle deformation is represented by the spring stiffness coefficient of the virtual spring. By theoretical modeling and experimental parameter identification of cutting force, the spring stiffness coefficients are obtained, thereby modeling the deflection of the needle. To verify the accuracy of the proposed model, the predicted model results were compared with the deflection of the puncture experiment in polyvinyl alcohol (PVA) gel samples, and the average maximum error range predicted by the model was between 0.606 ± 0.167 mm and 1.005 ± 0.174 mm, which showed that the model can successfully predict the deflection of the needle. This work will contribute to the design of automatic control strategies for needles.

Tetrodotoxin (TTX), a lethal neurotoxin, poses a grave threat to human health. The available spectroscopic methods suffer from limitations such as complex procedures and inadequate on-site capabilities. In this study, we proposed a method using Fe3O4@Cu as a catalytic biosensor combined with SERS, colorimetry and image processing for TTX detection. Integrating the aptamer amplifies the specificity of the system and masks the catalytic activity of Fe3O4@Cu. The catalytic efficiency of Fe3O4@Cu in the H2O2-TMB reaction can quantify the concentration of TTX in the system. Consequently, oxidation of TMB (oxTMB) led to the generation and change of signals for SERS, colorimetry and image processing, enabling a three-channel quantitative detection of TTX. Under the optimal conditions, the detection limit of established SERS, colorimetry and image processing were 0.055, 2.127 and 0.243 ng/mL, respectively. This three-channel biosensor was applied to real samples, providing an accurate, stable and adaptable alternative for on-site TTX detection.

In finishing simulations, achieving accurate results can be challenging due to the minimal amount of material removal and the limited measurement range of surface micro-topography instruments. To overcome these limitations, a novel high-fidelity modeling method combining image mosaic and wavelet decomposition technologies is proposed in this paper. We achieve the stitching of narrow field and high pixel micro morphology images through four steps: image feature extraction, overlapped feature matching, feature fusion, and stitching effect evaluation. On this basis, the wavelet decomposition method is employed to separate detection signals based on their respective frequencies, allowing the establishment of a datum plane and a roughness surface. The point cloud model undergoes a transformation into a continuous geometric model via the Poisson reconstruction algorithm. In the case study, four sample images of an aluminum alloy sheet after barrel finishing were collected using the ZeGage Plus optical profiler. Each image has an actual size of 834.37 μm × 834.37 μm. Subsequently, a comparison was carried out between the physical and simulation experiments. The results clearly indicate that the proposed method has the potential to enhance the accuracy of the finishing simulation by over 30%. The error between the resulting model and the actual surface of the part can be controlled within 1 μm.

Haze weather deteriorates image quality, causing images to become blurry with reduced contrast. This makes object edges and features unclear, leading to lower detection accuracy and reliability. To enhance haze removal effectiveness, we propose an image dehazing and fusion network based on the encoder-decoder paradigm (UIDF-Net). This network leverages the Image Fusion Module (MDL-IFM) to fuse the features of dehazed images, producing clearer results. Additionally, to better extract haze information, we introduce a haze encoder (Mist-Encode) that effectively processes different frequency features of images, improving the model\'s performance in image dehazing tasks. Experimental results demonstrate that the proposed model achieves superior dehazing performance compared to existing algorithms on outdoor datasets.

In particle image velocimetry (PIV) experiments, background noise inevitably exists in the particle images when a particle image is being captured or transmitted, which blurs the particle image, reduces the information entropy of the image, and finally makes the obtained flow field inaccurate. Taking a low-quality original particle image as the research object in this research, a frequency domain processing method based on wavelet decomposition and reconstruction was applied to perform particle image pre-processing. Information entropy analysis was used to evaluate the effect of image processing. The results showed that useful high-frequency particle information representing particle image details in the original particle image was effectively extracted and enhanced, and the image background noise was significantly weakened. Then, information entropy analysis of the image revealed that compared with the unprocessed original particle image, the reconstructed particle image contained more effective details of the particles with higher information entropy. Based on reconstructed particle images, a more accurate flow field can be obtained within a lower error range.