Real-time detection system for polishing metal surface defects based on convolutional feature concentration and activation network

With the rapid development of autonomous driving technology, traditional object detection algorithms often face challenges such as the trade-off between model parameters and recognition accuracy, insufficient detection accuracy, and slow detection speed when dealing with vehicle and pedestrian detection tasks in complex scenarios. To address these challenges, we propose a novel lightweight real-time driving scene detection network, FEEP-YOLO. Based on the YOLOv8 algorithm, our network not only maintains high detection accuracy but also achieves faster training speed and fewer model parameters. FEEP-YOLO employs a lightweight network, Faster Net, to significantly reduce model parameters, thereby accelerating detection speed and better meeting real-time requirements. To further improve detection accuracy, we introduce an EMA attention module into the network and optimize the bounding box loss function, adopting the EIOU loss function to enhance target features and reduce background interference. Additionally, to detect small objects more accurately, we add feature layers rich in semantic information to the detection end. Experimental results demonstrate that FEEP-YOLO outperforms YOLOv8 in terms of both detection speed and accuracy on the KITTI and SODA datasets, while significantly reducing the number of model parameters and model size. FEEP-YOLO achieves high detection speed while improving detection accuracy and reducing computational resource consumption, meeting the practical application requirements under limited computing power, memory space and power consumption. The key contribution of FEEP-YOLO lies in its ability to balance accuracy speed, and efficiency, making it a robust solution for real-time autonomous driving applications in complex environments.

As a key component in the hydraulic brake system of automobiles, the brake joint directly affects the braking performance and driving safety of the vehicle. Therefore, improving the quality of brake joints is crucial. During the processing, due to the complexity of the welding material and welding process, the weld seam is prone to various defects such as cracks, pores, undercutting, and incomplete fusion, which can weaken the joint and even lead to product failure. Traditional weld seam detection methods include destructive testing and non-destructive testing; however, destructive testing has high costs and long cycles, and non-destructive testing, such as radiographic testing and ultrasonic testing, also have problems such as high consumable costs, slow detection speed, or high requirements for operator experience. In response to these challenges, this article proposes a defect detection and classification method for laser welding seams of automotive brake joints based on machine vision inspection technology. Laser-welded automotive brake joints are subjected to weld defect detection and classification, and image processing algorithms are optimized to improve the accuracy of detection and failure analysis by utilizing the high efficiency, low cost, flexibility, and automation advantages of machine vision technology. This article first analyzes the common types of weld defects in laser welding of automotive brake joints, including craters, holes, and nibbling, and explores the causes and characteristics of these defects. Then, an image processing algorithm suitable for laser welding of automotive brake joints was studied, including pre-processing steps such as image smoothing, image enhancement, threshold segmentation, and morphological processing, to extract feature parameters of weld defects. On this basis, a welding seam defect detection and classification system based on the cascade classifier and AdaBoost algorithm was designed, and efficient recognition and classification of welding seam defects were achieved by training the cascade classifier. The results show that the system can accurately identify and distinguish pits, holes, and undercutting defects in welds, with an average classification accuracy of over 90%. The detection and recognition rate of pit defects reaches 100%, and the detection accuracy of undercutting defects is 92.6%. And the overall missed detection rate is less than 3%, with both the missed detection rate and false detection rate for pit defects being 0%. The average detection time for each image is 0.24 s, meeting the real-time requirements of industrial automation. Compared with infrared and ultrasonic detection methods, the proposed machine-vision-based detection system has significant advantages in detection speed, surface defect recognition accuracy, and industrial adaptability. This provides an efficient and accurate solution for laser welding defect detection of automotive brake joints.

In the process of steel production, the defects on the surface of steel will adversely affect the subsequent processing of a product. Accurate detection of such defects is the key to improve production efficiency and economic benefits. In this paper, an end-to-end steel surface defect detection and size measurement system based on the YOLOv5 model is designed. Firstly, in consideration of the defect location and direction correlation in the production process, a coordinate attention mechanism is added at the head of YOLOv5 to strengthen the spatial correlation of the steel surface and an adaptive anchor box generation method based on defect shape difference feature is proposed, which realizes the detection of three main types of defects on the Pytorch deep learning framework. Secondly, BiFPN is used to strengthen the feature fusion and a transformer encoder is added to improve the performance of detecting small defects. Thirdly, calculate the conversion ratio between the pixel and the actual size according to the standard reference specimen and obtain the actual size through the pixel statistics of the defect area to achieve pixel level size measurement. Finally, the steel surface defect detection and size measurement system are designed in this paper, which consist of various hardware, related measurement, and detection algorithms. According to the experimental results, the comprehensive defect detection accuracy of this method reaches 93.6%, of which the scratch detection accuracy reaches 95.7%. The detection speed reaches 133 fps and the defect size measurement accuracy reaches 0.5 mm. Experimental result shows that the defect detection and size measurement system designed in this paper can accurately detect and measure various industrial production defects and can be applied to the actual production process.

An investigation was made of the conditions for the formation of an optical breakdown plasma in air when CO2 laser radiation interacted with a polished metal surface in the case of two strongly differing widths of the irradiation spot. Metallographic investigations were made of the surface of the irradiated samples. For the first time, the specific form was established of the surface defects which lowered the breakdown thresholds.

For real applications of pedestrian detection, both detection speed and detection accuracy are important. In this paper we propose a detector based on effective comparison features (ECFs) for simultaneously improving detection accuracy and speed. ECFs are defined as the features helping to improve actual performance. Using only these ECFs as feature candidates for the split nodes of decision trees, our detector can achieve accurate results. As an additional benefit, detection speed is improved by earlier rejection of negative samples. Experiments are conducted using well-known benchmark datasets for pedestrian detection. The experimental results of our ECF detector show that our detection speed is 1–2 orders of magnitude faster than the speed of state-of-the-art algorithms, with comparable detection accuracy.

In the field of unmanned aerial vehicle-based transmission line inspection, the neural network models currently employed suffer from large model sizes and high computational requirements, resulting in a trade-off between detection speed and accuracy. We propose an improved lightweight YOLOv5 object detection algorithm based on channel pruning to address this issue. The approach incorporates the SimAM attention mechanism into the backbone network to enhance feature extraction capability without increasing computational complexity, thereby improving detection accuracy. Traditional convolutional modules in the neck network are replaced with GSconv modules to reduce convolutional computation and enhance detection speed. The Wise-IoU loss is utilized as the localization loss for the detection model, speeding up model convergence. Finally, a model compression technique is implemented to eliminate unnecessary channels within the network, resulting in a decrease in model size and a boost in detection speed. Experimental results on a constructed grid inspection dataset demonstrate that the improved model achieves a 72.1% reduction in model size, a 62.5% reduction in GFLOPs, a 5.9 FPS improvement, and a 0.3% mAP increase compared to the original YOLOv5. These findings underscore the advantages of the improved lightweight YOLOv5 object detection algorithm, offering a blend of heightened accuracy, streamlined size, and swift detection capabilities.

Rubber-sleeving a forceps for endoscopic removal of a flat, metallic foreign body

Boiling bubble formation analysis in saturated water at artificial nucleation sites drilled in polished metal surface

We describe a new algorithm for occupancy grid mapping using LIDAR in the presence of glass and other non-diffuse surfaces. This is a major problem for robot navigation in many indoor environments due to the prevalence of glass paned doors, windows, and even glass walls, as well as mirrors and polished metal surfaces. Current formulations of occupancy grid mapping make the assumption that objects in the environment are detectable from all angles. However, glass and other specular surfaces are invisible to LIDAR at most angles and so become washed out as “noise”. We modify the standard occupancy grid algorithm to allow for mapping objects that are only visible from certain view angles, by tracking the subset of angles from which objects are reliably visible. We show that these angles can be determined reliably with a single pass through the environment, and that the information can be used to map both diffuse and specular surfaces.

Printed circuit boards (PCBs) are primarily used to connect electronic components to each other. It is one of the most important stages in the manufacturing of electronic products. A small defect in the PCB can make the final product inoperable. Therefore, careful and meticulous defect detection steps are necessary and indispensable in the PCB manufacturing process. The detection methods can generally be divided into manual inspection and automatic optical inspection (AOI). The main disadvantage of manual detection is that the detection speed is too slow, resulting in a waste of human resources and costs. Thus, in order to speed up the production speed, AOI techniques have been adopted by many PCB manufacturers. Most current AOI mechanisms use traditional optical algorithms. These algorithms can easily lead to misjudgments due to different light and shadow changes caused by slight differences in PCB placement or solder amount so that qualified PCBs are judged as defective products, which is also the main reason for the high misjudgment rate of AOI detection. In order to effectively solve the problem of AOI misjudgment, manual re-judgment is currently the reinspection method adopted by most PCB manufacturers for defective products judged by AOI. Undoubtedly, the need for inspectors is another kind of labor cost. To reduce the labor cost. of manual re-judgement, an accurate and efficient PCB defect reinspection mechanism based on deep learning algorithm is proposed. This mechanism mainly establishes two detection models, which can classify the defects of the product. When both models have basic recognition capabilities, the two models are then combined into a main model to improve the accuracy of defect detection. In the study, the data provided by Lite-On Technology Co., Ltd. were implemented. To achieve the practical application value in the industry, this research not only considers the problem of detection accuracy, but also considers the problem of detection execution speed. Therefore, fewer parameters are used in the construction of the model. The research results show that the accuracy rate of defect detection is about 95%, and the recall rate is 94%. Compared with other detection modules, the execution speed is greatly improved. The detection time of each image is only 0.027 s, which fully meets the purpose of industrial practical application.

As industrial production becomes more modern and intelligent today, the inspection of product quality of the workshop is becoming more and more accustomed to replacing the old manual visual inspection methods with automated inspection systems. In the welding field, automated welding robots are not only used in traditional large-scale automobile assembly lines. In more general welding work, welding robots also plays an important role. The inspection of the welding quality of the welding robot is mainly to detect the four main types of weld defects. Compared to traditional defect classification based on support vector machines and defect detection based on template matching, this paper uses a welding surface defect detection system designed based on deep learning methods. By working with workshop welding experts, a large-scale image of nearly 5000 pictures is built. Large-scale weld defect datasets, while using the real-time and accuracy of the YOLO series of deep learning object detection frameworks, the weld defects detection model reaches 75.5% mean average precision(mAP) in constructed weld defect data set. In addition, the construction cost of the detection model and the deployment time of the detection system are greatly reduced. During the field test of the system in the workshop, among a batch of welding workpieces provided by the factory, the detection accuracy of weld defects reached 71%, which initially met the requirements of the workshop for an automated defect detection system.

ABSTRACTThe defect detection of prefabricated building components during production and use is the key to ensuring building quality. Traditional manual methods are inefficient and prone to missed detections, while existing deep learning models face challenges such as insufficient detection accuracy, high computational resource consumption, and poor real‐time performance in complex backgrounds and small target scenes. To address this issue, this study uses YOLOv5 as the basic object detection algorithm, introduces an attention mechanism, replaces the loss function, and improves the Adam optimizer to obtain YOLOv5s. To reduce the size of the YOLOv5s, GhostNet is introduced as a feature extraction network to construct the YOLOv5s‐GhostNet detection model for defect detection of prefabricated building components. In actual defect detection of building components, the YOLOv5s‐GhostNet researched and designed only had 12 defect classification errors, and the classification accuracy was improved to 99.6%. The proposed model had an AUC area of 0.90 for defect detection in building components and a detection speed of 42FPS. In visual analysis, the confidence coefficient of the proposed model for detecting “crack” defects was 99%. YOLOv5s‐GhostNet has extremely high accuracy and efficiency in detecting defects in prefabricated building components and has broad application prospects. In order to verify the performance of the YOLOv5s GhostNet model proposed in this article, traditional YOLOv5, VGG‐16, ResNet, and HRNet were selected as benchmark models for comparison. The experimental results show that the proposed model outperforms these benchmark models in terms of detection accuracy, detection speed, and model complexity, further verifying its effectiveness and superiority in practical applications. The application of this model not only promotes the further development of deep learning technology in architecture but also provides a reference for defect detection in other fields.

As the core component of electronic products, the quality of PCB (print circuit board) directly affects the performance and reliability of the entire product. The traditional PCB defect detection methods mainly rely on manual visual inspection and image processing technology, which have problems such as low efficiency and easy errors and cannot meet the needs of large-scale production. The PCB defect detection method based on deep learning and object detection technology has gradually become mainstream. This article focuses on the research of PCB defect object detection method based on deep learning. Therefore, the paper conducts relevant intensive study on PCB defect detection, aiming to develop a model that is accurate, compact, and suitable for real-time detection. Specific improvements include using C2F_The ViT module replaces the original C2F, and the purpose is to facilitate the model to comprehensively consider the local details of PCB defects and the global contextual semantic information. Introduces multi-head self-attention mechanism (MHSA), adds detection heads in the p2 layer, and improves defect detection accuracy and location accuracy. Extract the target semantic information of small defects and suppress the local background information at the same time. The experimental results indicate that the detection algorithm can improve detection accuracy. Compared with the original YOLOv8, the improved model has a 4.9% and 7.9% improvement in map50 and map50-95, respectively.

Accurate and rapid detection of anatomical structures, such as the glottis, is critical during tracheal intubation (TI) to ensure patient safety and procedural success. However, it remains a challenge due to the limited field of view and computational resources of video laryngoscopy, especially for difficult airway situations. Existing deep learning (DL) models struggle to balance high accuracy and real-time clinical deployment. To address these issues, we propose TI-YOLO (TI-You Only Look Once), a lightweight and efficient object detection model built upon the YOLOv11 architecture. TI-YOLO introduces the Bidirectional Feature Pyramid Network (BiFPN) module for multi-scale feature fusion, effectively enhancing the ability to detect anatomical structures of different sizes. TI-YOLO integrates the Deformable Attention Transformer (DAT) module to enhance the perception of crucial regions, improving detection accuracy and robustness. To further reduce the consumption of computational resources while maintaining efficiency, TI-YOLO is optimized by reconstructing the backbone based on MobileNetV4. Furthermore, TI-YOLO employs the Slide Weight Function (SWF) as a loss function during model training to mitigate the class imbalance within the dataset. One self-built dataset is used to validate the effectiveness of TI-YOLO. Compared to the original YOLOv11, TI-YOLO achieves mean Average Precision at IoU 0.50 (mAP50) scores of 0.902, with improvements of 3.8%. Meanwhile, TI-YOLO balances detection accuracy and computational efficiency with a 10.5% reduction in floating-point operations (FLOPs) and a 28.9% reduction in parameters, and the model weight is only 4.6 MB. Additionally, to evaluate TI-YOLO real-time inference capability, we quantize and deploy it on a low-cost embedded OrangePi 5 platform. The inference speed reaches over 50 frames per second (FPS), meeting real-time clinical requirements.

Underwater Wireless Sensor Networks (UWSNs) are a pivotal technology for ocean monitoring, underwater surveillance, and environmental sensing. However, these networks are vulnerable to numerous faults caused by adverse aquatic environments, scarce node resources, and unreliable acoustic communication. This paper presents an adaptive fault detection and tolerance mechanism specially designed for UWSNs. The proposed system integrates a hybrid fault detection approach, combining explicit and implicit approaches with a light-weight tolerance strategy leveraging multi-path rerouting and load-aware decision-making. A dynamic thresholding mechanism is used to enhance fault detection sensitivity and accuracy while ensuring energy efficiency. The mechanism is implemented and evaluated using MATLAB simulations with different fault densities and node behaviors. The findings show significant improvement in detection accuracy, less false alarm, lower latency, and better network lifetime when compared with traditional fault management schemes. This research is a step toward constructing fault-tolerant UWSNs that can maintain operations under the rough and uncertain nature of underwater environments.