An Improved Safety Belt Detection Algorithm for High-Altitude Work Based on YOLOv8

Safety belt nonuse by internal medicine patients: A missed opportunity in clinical preventive medicine

Cost savings associated with increased safety belt use in Iowa, 1987–1988

The symmetry between production efficiency and safety is a crucial aspect of industrial operations. To enhance the identification of proper safety harness use by workers at height, this study introduces a machine vision approach as a substitute for manual supervision. By focusing on the safety rope that connects the worker to an anchor point, we propose a semantic segmentation mask annotation principle to evaluate proper harness use. We introduce CEMFormer, a novel semantic segmentation model utilizing ConvNeXt as the backbone, which surpasses the traditional ResNet in accuracy. Efficient Multi-Scale Attention (EMA) is incorporated to optimize channel weights and integrate spatial information. Mask2Former serves as the segmentation head, enhanced by Poly Loss for classification and Log-Cosh Dice Loss for mask loss, thereby improving training efficiency. Experimental results indicate that CEMFormer achieves a mean accuracy of 92.31%, surpassing the baseline and five state-of-the-art models. Ablation studies underscore the contribution of each component to the model’s accuracy, demonstrating the effectiveness of the proposed approach in ensuring worker safety.

Reviews of evidence regarding interventions to increase the use of safety belts

This synthesis will be useful to commercial vehicle operators, federal and state agencies, and others interested in improving commercial vehicle safety. The report identifies and documents (1) motivating factors that influence commercial motor vehicle (CMV) drivers in deciding whether to wear safety belts and (2) research and practices that address CMV safety belt usage. It also offers a review of ergonomic and human engineering factors in the design and use of safety belts in CMVs as well as approaches to facilitate safety belt use by truck manufacturers. The information for this synthesis was obtained through a review of relevant literature; a survey of commercial truck and bus fleet managers; focus group discussions with CMV drivers; and visits to a number of CMV original equipment manufacturers (OEMs) to examine installation of current safety belt designs and approaches used in new CMVs. This synthesis was prepared in support of the U.S. Department of Transportation Federal Motor Carrier Safety Administration's Commercial Motor Vehicle Safety Belt Partnership, which is conducting outreach activities to promote safety belt use and participating in and implementing safety belt-related research.

Traumatic cardiac and thoracic aortic injuries are hypothesized to result from rapid deceleration of occupants during a motor vehicle crash. The purpose of this study was to identify potential risk factors for motor vehicle-related cardiac and thoracic aortic (HTA) injury using the Crash Injury Research Engineering Network (CIREN) database. CIREN data were used to test the hypothesis that there is no difference between occupants with HTA injury and occupants with thoracic injury other than the heart or aorta (non-HTA). Occupant variables (restraint use, airbag deployment, Glasgow Coma Scale score, Injury Severity Score, concomitant injuries, driver versus passenger status, height, and comorbidity) and crash variables (principal direction of force, change in velocity, vehicle crush, intrusion, and vehicle type) were compared for these two groups. Odds ratios were used to quantify the potential risk factors for HTA injury compared with non-HTA injury. There were 168 occupants with an HTA injury and 731 with a non-HTA injury. Greater crash severity (based on vehicle crush and change in velocity), improper safety belt use, and lack of safety belt use were significantly associated with HTA injury. Unrestrained occupants had almost three times the chance of having an HTA injury (odds ratio = 2.86; p < 0.05). For restrained drivers, 41.4% of HTA injuries were caused by vehicle interior components. When not protected by both safety belts and air bags, 45.7% of driver HTA injuries were caused by the steering wheel. For passengers, the vehicle interior (armrests, side interior, and B-pillars) accounted for most HTA injuries regardless of safety system status. More than half of all occupants wearing safety belts who sustained an HTA injury were improperly wearing their safety belts. The high mortality associated with cardiac and aortic injuries supports the need to prevent these injuries from occurring during motor vehicle crashes. These results suggest proper use of safety belts is necessary to prevent cardiac and thoracic aortic injuries. However, other important potential risk factors, such as motor vehicle size and crash severity, might continue to present a challenge to motor vehicle safety professionals.

Safety is the eternal theme of power systems. In view of problems such as time-consuming and poor real-time performance in the correct use of seat belt hooks by manual supervision operators in the process of power operation, this paper proposes an improved YOLOv7 seat belt hook suspension state recognition algorithm. Firstly, the feature extraction part of the YOLOv7 backbone network is improved, and the M-Spatial Pyramid Pooling Concurrent Spatial Pyramid Convolution (M-SPPCSPC) feature extraction module is constructed to replace the Spatial Pyramid Pooling Concurrent Spatial Pyramid Convolution (SPPCSPC) module of the backbone network, which reduces the amount of computation and improves the detection speed of the backbone network while keeping the sensory field of the backbone network unchanged. Second, a decoupled head, which realizes the confidence and regression frames separately, is introduced to alleviate the negative impact of the conflict between the classification and regression tasks, consequently improving the network detection accuracy and accelerating the network convergence. Ultimately, a dynamic non-monotonic focusing mechanism is introduced in the output layer, and the Wise Intersection over Union (WioU) loss function is used to reduce the competitiveness of high-quality anchor frames while reducing the harmful gradient generated by low-quality anchor frames, which ultimately improves the overall performance of the detection network. The experimental results show that the mean Average Precision (mAP@0.5) value of the improved network reaches 81.2%, which is 7.4% higher than that of the original YOLOv7, therefore achieving better detection results for multiple-state recognition of hooks.

Ensuring worker safety through compliance with Personal Protective Equipment (PPE) mandates, particularly the use of safety helmets, is a critical concern in the construction industry. Automated surveillance systems leveraging advanced object detection models offer a promising solution to enhance monitoring and reduce accidents. This paper presents a comparative performance evaluation of two promi- nent YOLO (You Only Look Once) variants, YOLOv8 and YOLOv9, for the specific task of safety helmet detection. Utilizing a publicly available hard-hat dataset featuring diverse construction site scenarios, both models were trained for 7 epochs and rigorously evaluated. Performance metrics, including precision, recall, mean Average Precision (mAP), F1-score, and confusion matrices, were analyzed. The experimental results indicate that YOLOv9 exhibits a marginal but consistent performance advantage over YOLOv8, achieving an mAP@0.5 of 66.70% compared to YOLOv8’s 65.73%, and an F1-score of 76.15% versus 75.66%. This study underscores the incremental improvements in the YOLO architecture and provides valuable insights for selecting robust models for real-world safety monitoring applications. While both models demonstrate high precision, the relatively lower recall suggests areas for future improvement through more extensive training or model fine-tuning to enhance detection rates in safety-critical environments.

Volcanism is an important geological evolutionary process on the Moon. The study of lunar volcanic features is of great significance and value to understanding the geological evolution of the Moon better. Lunar domes are one of the essential volcanic features of the Moon. However, the existing lunar dome detection methods are still traditional manual or semiautomatic identification approaches that require extensive prior knowledge and have a complex identification process. Therefore, this paper proposes an automatic detection method based on improved YOLOv7 for lunar dome detection. First, a new lunar dome dataset was created by digital elevation model (DEM) data, and the effective squeeze and excitation (ESE) attention mechanism module was added to the backbone and neck sections to reduce information loss in the feature map and enhance network expressiveness. Then, a new SPPCSPC-RFE module was proposed by adding the receptive field enhancement (RFE) module into the neck section, which can adapt to dome feature maps of different shapes and sizes. Finally, the bounding box regression loss function complete IOU (CIOU) was replaced by wise IOU (WIOU). The WIOU loss function improved the model's performance for the dome detection effect. Furthermore, this study combined several data enhancement strategies to improve the robustness of the network. To evaluate the performance of the proposed model, we conducted several experiments using the dome dataset developed in this study. The experimental results indicate that the improved method outperforms related methods with a mean average precision (mAP@0.5) value of 88.7%, precision (P) value of 85.6%, and recall (R) value of 86.4%. This study provides an effective solution for lunar dome detection.

Bug or Fault localization is a process of identifying the specific location(s) or region(s) of source code (at various granularity levels such as the directory path, file, method or statement) that is faulty and needs to be modified to repair the defect. Fault localization is a routine task in software maintenance (corrective maintenance). Due to the increasing size and complexity of current software applications, automated solutions for fault localization can significantly reduce human effort and software maintenance cost.We present a technique (which falls into the class of static techniques for bug localization) for fault localization based on a character n-gram based Information Retrieval (IR) model. We frame the problem of bug localization as a relevant document(s) search task for a given query and investigate the application of character-level n-gram based textual features derived from bug reports and source-code file attributes. We implement the proposed IR model and evaluate its performance on dataset downloaded from two popular open-source projects (JBoss and Apache).We conduct a series of experiments to validate our hypothesis and present evidences to demonstrate that the proposed approach is effective. The accuracy of the proposed approach is measured in terms of the standard and commonly used SCORE and MAP (Mean Average Precision) metrics for the task of fault localization. Experimental results reveal that the median value for the SCORE metric for JBoss and Apache dataset is 99.03% and 93.70% respectively. We observe that for 16.16% of the bug reports in the JBoss dataset and for 10.67% of the bug reports in the Apache dataset, the average precision value (computed at all recall levels) is between 0.9 and 1.0.

In-process vision monitoring methods for aircraft coating laser cleaning based on deep learning

As people have higher requirements for object detection accuracy and adaptability, this research proposes a single-stage algorithm for thermal image target detection based on Yolo v4. The algorithm combines the visible light image and the thermal image to complement the image information, and uses the TMDF (Two Modol Differential Fusion) module to perform feature fusion on the visible light image and the thermal image and then enter the network for training. Adding the DCT(Discrete Cosine Transform) adjustment network to adjust the final output can finally increase the mAP (mean Average Precision) value of the model on the KAIST data set by 3.7&#x0025;, and MR (missed detection rate) value decreased by 3.5&#x0025;.

To address the current problems of large positioning error, low picking efficiency, and high cost of tea shoot picking, a continuous and precise harvesting scheme for tea shoots based on a two-dimensional (2D) perspective is designed in this study. A high-speed harvesting method for tea shoots in a standardized tea plantation assembly line type was proposed. First, a 2D view recognition model of tea shoot tips in a multi-disturbance environment was constructed, and accurate picking point coordinates were determined by combining a skeleton algorithm and curve growth. To avoid the losses of recognition accuracy caused by the mistaken clamping of blades and vibrations during harvester operations, accurate control of the harvester was realized by combining path planning and the S-curve speed control function. The recognition accuracy for the verification set of the recognition model was 99.9%, and the mean average precision (0.5:0.95) value was 0.97. The test results show that the error between the actual picking point position and the position determined by the model was within ± 3 mm, and the picking success rate was 83.6%. Therefore, we can realize fast and accurate picking of tea shoots and lay the foundation for continuous tea picking in the future by simplifying the identification and picking process.

The complex interaction between laser and material in Laser Wire Direct Energy Deposition (LW-DED) Additive Manufacturing (AM) benefits from process monitoring methods to ensure process stability and final part quality. Understanding the relationship between process parameters and melt pool geometrical characteristics can be used to effectively monitor and in-process control the process, as the melt pool geometrical characteristics serve as crucial indicators of process stability and quality.This study presents a novel in-situ monitoring approach for LW-DED, utilizing process images for melt pool segmentation, melt pool geometrical characteristics estimation, process stability assessment, and bead geometry prediction. The segmentation of melt pool objects was successfully accomplished using Convolutional Neural Networks (CNN)-based models, enabling the prediction of essential parameters such as melt pool area, height, width, center of area, and the center point of the bounding box enclosing the melt pool. Multiple models were compared regarding the accuracy and processing speed using a controlled central composite design and random experiments. We used an Inconel alloy 625 wire and two distinct substrate materials for deposition, a coaxial laser welding head with a 3 kW fiber laser, and an off-axis welding camera for monitoring.Among the CNN architectures evaluated, YOLOv8l demonstrated superior accuracy with mean Average Precision (mAP) values of 0.925 and 0.853 for Stainless Steel (SS) and low carbon steel (S355) substrates, respectively. Additionally, YOLOv8s exhibited a notable processing speed of over 114 frames per second, which indicates its suitability for real-time process control. Furthermore, the results indicate a significant correlation between process parameters and melt pool geometry variables. Notably, a clear correlation was established between melt pool characteristics and bead geometries obtained through microscopic examinations, including penetration depth and heat-affected zone. The analysis revealed a significant correlation for the bead area and width parameters. In relation to the bead height, while the correlation exhibited a lower magnitude compared to bead area and width, it remained responsive. In addition, the tensor masks derived from the developed models have proven to be highly effective in accurately predicting bead geometries.The results demonstrate the effectiveness of YOLO-based algorithms for detecting and segmenting the melt pool. Statistical analysis confirms the significance of stabilized process data and the accuracy of melt pool geometric models. We demonstrate that integrating advanced monitoring and control techniques using artificial intelligence methods like CNN can facilitate process stability and quality control.

With the continuous development of science and technology, cylindrical lithium batteries, as new energy batteries, are widely used in many fields. In the production process of lithium batteries, various defects may occur. To detect the defects of lithium batteries, a detection algorithm based on convolutional neural networks is proposed in this paper. Firstly, image preprocessing is introduced on the collected lithium battery dataset. Secondly, the K-means clustering algorithm is used on the processed dataset to generate anchor boxes for lithium battery defect detection. Then the detection network YOLOv3 is trained with the given dataset. Finally, the detection network YOLOv3 is applied to output the type and location information of the defect. The experimental results show that the mean average precision (mAP) value of the detection algorithm on the lithium battery validation dataset reaches 94% and the detection speed is 25 frames per second. The proposed algorithm can effectively locate and classify the bottom defects of the lithium battery.