High Detection Accuracy Research Articles

Early Sweet Potato Plant Detection Method Based on YOLOv8s (ESPPD-YOLO): A Model for Early Sweet Potato Plant Detection in a Complex Field Environment

Traditional methods of pest control for sweet potatoes cause the waste of pesticides and land pollution, but the target detection algorithm based on deep learning can control the precise spraying of pesticides on sweet potato plants and prevent most pesticides from entering the land. Aiming at the problems of low detection accuracy of sweet potato plants and the complex of target detection models in natural environments, an improved algorithm based on YOLOv8s is proposed, which can accurately identify early sweet potato plants. First, this method uses an efficient network model to enhance the information flow in the channel, obtain more effective global features in the high-level semantic structure, and reduce model parameters and computational complexity. Then, cross-scale feature fusion and the general efficient aggregation architecture are used to further enhance the network feature extraction capability. Finally, the loss function is replaced with InnerFocaler-IoU (IFIoU) to improve the convergence speed and robustness of the model. Experimental results showed that the mAP0.5 and model size of the improved network reached 96.3% and 7.6 MB. Compared with the YOLOv8s baseline network, the number of parameters was reduced by 67.8%, the amount of computation was reduced by 53.1%, and the mAP0.5:0.95 increased by 3.5%. The improved algorithm has higher detection accuracy and a lower parameter and calculation amount. This method realizes the accurate detection of sweet potato plants in the natural environment and provides technical support and guidance for reducing pesticide waste and pesticide pollution.

Self‐Attention Mechanisms‐Based Laryngoscopy Image Classification Technique for Laryngeal Cancer Detection

ABSTRACTBackgroundThe early diagnosis of laryngeal cancer (LCA) is crucial for prognosis, driving our search for an accurate, precise, and sensitive deep learning model to assist in LCA detection.MethodsWe collected 5768 laryngoscopic images from 1462 patients and created the intelligent laryngeal cancer detection system (ILCDS) based on Swin‐Transformer. Following training and validation, we assessed the ILCDS performance on the internal and external test sets and compared it with previous convolutional neural network (CNN) models and three professional laryngologists.ResultsThe ILCDS outperformed the six CNNs, with the highest accuracy of 92.78% and an area under the curve (AUC) of 0.9732. Despite a slight drop in performance on external sets, the ILCDS maintained the best superiority, with 85.79% accuracy and an AUC of 0.9550. Surpassing professional laryngologists, the ILCDS achieved 92.00% accuracy.ConclusionsThe ILCDS offers high accuracy and stability for LCA detection, reducing the burden on laryngologists.

Mf-net: multi-feature fusion network based on two-stream extraction and multi-scale enhancement for face forgery detection

Due to the increasing sophistication of face forgery techniques, the images generated are becoming more and more realistic and difficult for human eyes to distinguish. These face forgery techniques can cause problems such as fraud and social engineering attacks in facial recognition and identity verification areas. Therefore, researchers have worked on face forgery detection studies and have made significant progress. Current face forgery detection algorithms achieve high detection accuracy within-dataset. However, it is difficult to achieve satisfactory generalization performance in cross-dataset scenarios. In order to improve the cross-dataset detection performance of the model, this paper proposes a multi-feature fusion network based on two-stream extraction and multi-scale enhancement. First, we design a two-stream feature extraction module to obtain richer feature information. Secondly, the multi-scale feature enhancement module is proposed to focus the model more on information related to the current sub-region from different scales. Finally, the forgery detection module calculates the overlap between the features of the input image and real images during the training phase to determine the forgery regions. The method encourages the model to mine forgery features and learns generic and robust features not limited to a particular feature. Thus, the model achieves high detection accuracy and performance. We achieve the AUC of 99.70% and 90.71% on FaceForensics++ and WildDeepfake datasets. The generalization experiments on Celeb-DF-v2 and WildDeepfake datasets achieve the AUC of 80.16% and 65.15%. Comparison experiments with multiple methods on other benchmark datasets confirm the superior generalization performance of our proposed method while ensuring model detection accuracy. Our code can be found at: https://github.com/1241128239/MFNet.

Advancements in Target Detection: A Comparative Analysis of Deep Learning Models for Real-Time Applications

Abstract. Target detection is a vital field within computer vision, playing an essential role in applications. This paper investigates the advancements and efficiencies of contemporary target detection methodologies, focusing on deep learning frameworks. Through a systematic review and evaluation of these models' architectures and performances using the Common Objects in Context (COCO) dataset, the study highlights their operational effectiveness and practical implications in real-world scenarios. A detailed comparative analysis is conducted, assessing the models based on mean Average Precision (mAP) and input dimensions to determine their suitability across various detection tasks. You Only Look Once version 3 (YOLOv3), in particular, is recognized for its ability to combine high-speed detection with significant accuracy, effectively addressing real-time processing challenges. The results confirm that YOLOv3, despite its smaller input size, performs comparably to more complex systems, demonstrating notable enhancements in design efficiency and processing speed. This research underscores the potential for future optimizations in model architectures to bridge the gap between high-speed and high-accuracy detection tasks, potentially transforming real-time applications across multiple sectors. The practical significance of this work lies in its ability to guide future developments in object detection, benefiting both academic research and industrial applications.

Revisiting streaming anomaly detection: benchmark and evaluation

Anomaly detection in streaming data is an important task for many real-world applications, such as network security, fraud detection, and system monitoring. However, streaming data often exhibit concept drift, which means that the data distribution changes over time. This poses a significant challenge for many anomaly detection algorithms, as they need to adapt to the evolving data to maintain high detection accuracy. Existing streaming anomaly detection algorithms lack a unified evaluation framework that validly assesses their performance and robustness under different types of concept drifts and anomalies. In this paper, we conduct a systematic technical review of the state-of-the-art methods for anomaly detection in streaming data. We propose a new data generator, called SCAR (Streaming data generator with Customizable Anomalies and concept dRifts), that can synthesize streaming data based on synthetic and real-world datasets from different domains. Furthermore, we adapt four static anomaly detection models to the streaming setting using a generic reconstruction strategy as baselines, and then compare them systematically with 9 existing streaming anomaly detection algorithms on 76 synthesized datasets that have various types of anomalies and concept drifts. The challenges and future research directions for anomaly detection in streaming data are also presented.

An Improved UNet-Based Path Recognition Method in Low-Light Environments

The fruit industry is a significant economic sector in China, with modern orchards gradually transitioning to trellis orchards. For mechanized orchard operations, automatic vehicle navigation is essential. However, in trellis orchards, the shading from trees results in low average light intensity and large variations in lighting, posing challenges for path navigation. To address this, a path navigation algorithm for trellis orchards is proposed based on the UNet-CBAM model. The network structures of UNet, FCN, and SegNet are compared to identify and select the optimal structure for further improvement. Among the three attention mechanisms of channel attention, spatial attention, and combined attention, the most effective mechanism is identified. The optimal attention mechanism is incorporated into the optimized network to enhance the model’s ability to detect path edges and improve detection performance. To validate the effectiveness and generalizability of the model, a total of 400 images were collected under varying lighting intensities. The experimental results show that this method achieves an accuracy of 97.63%, a recall of 93.94%, and an Intersection over Union (IoU) of 92.19%. These results significantly enhance path recognition accuracy in trellis orchards, particularly under low light under conditions. Compared to the FCN and SegNet algorithms, this method provides higher detection accuracy and offers a new theoretical foundation and research approach for path recognition in low-light environments.

LG-YOLOv8: A Lightweight Safety Helmet Detection Algorithm Combined with Feature Enhancement

In the realm of construction site monitoring, ensuring the proper use of safety helmets is crucial. Addressing the issues of high parameter values and sluggish detection speed in current safety helmet detection algorithms, a feature-enhanced lightweight algorithm, LG-YOLOv8, was introduced. Firstly, we introduce C2f-GhostDynamicConv as a powerful tool. This module enhances feature extraction to represent safety helmet wearing features, aiming to improve the efficiency of computing resource utilization. Secondly, the Bi-directional Feature Pyramid (BiFPN) was employed to further enrich the feature information, integrating feature maps from various levels to achieve more comprehensive semantic information. Finally, to enhance the training speed of the model and achieve a more lightweight outcome, we introduce a novel lightweight asymmetric detection head (LADH-Head) to optimize the original YOLOv8-n’s detection head. Evaluations on the SWHD dataset confirm the effectiveness of the LG-YOLOv8 algorithm. Compared to the original YOLOv8-n algorithm, our approach achieves a mean Average Precision (mAP) of 94.1%, a 59.8% reduction in parameters, a 54.3% decrease in FLOPs, a 44.2% increase in FPS, and a 2.7 MB compression of the model size. Therefore, LG-YOLOv8 has high accuracy and fast detection speed for safety helmet detection, which realizes real-time accurate detection of safety helmets and an ideal lightweight effect.

CSTAN: A Deepfake Detection Network with CST Attention for Superior Generalization

With the advancement of deepfake forgery technology, highly realistic fake faces have posed serious security risks to sensor-based facial recognition systems. Recent deepfake detection models mainly use binary classification models based on deep learning. Despite achieving high detection accuracy on intra-datasets, these models lack generalization ability when applied to cross-datasets. We propose a deepfake detection model named Channel-Spatial-Triplet Attention Network (CSTAN), which focuses on the difference between real and fake features, thereby enhancing the generality of the detection model. To enhance the feature-learning ability of the model for image forgery regions, we have designed the Channel-Spatial-Triplet (CST) attention mechanism, which extracts subtle local information by capturing feature channels and the spatial correlation of three different scales. Additionally, we propose a novel feature extraction method, OD-ResNet-34, by embedding ODConv into the feature extraction network to enhance its dynamic adaptability to data features. Trained on the FF++ dataset and tested on the Celeb-DF-v1 and Celeb-DF-v2 datasets, the experimental results show that our model has stronger generalization ability in cross-datasets than similar models.

Ventricular Arrhythmia Classification Using Similarity Maps and Hierarchical Multi-Stream Deep Learning.

Ventricular arrhythmias are the primary arrhythmias that cause sudden cardiac death. We address the problem of classification between ventricular tachycardia (VT), ventricular fibrillation (VF) and non-ventricular rhythms (NVR). To address the challenging problem of the discrimination between VT and VF, we develop similarity maps - a novel set of features designed to capture regularity within an ECG trace. These similarity maps are combined with features extracted through learnable Parzen band-pass filters and derivative features to discriminate between VT, VF, and NVR. To combine the benefits of these different features, we propose a hierarchical multi-stream ResNet34 architecture. Our empirical results demonstrate that the similarity maps significantly improve the accuracy of distinguishing between VT and VF. Overall, the proposed approach achieves an average class sensitivity of 89.68%, and individual class sensitivities of 81.46% for VT, 89.29% for VF, and 98.28% for NVR. The proposed method achieves a high accuracy of ventricular arrhythmia detection and classification. Correct detection and classification of ventricular fibrillation and ventricular tachycardia are essential for effective intervention and for the development of new therapies and translational medicine.

Deep learning method to automatically diagnose periodontal bone loss and periodontitis stage in dental panoramic radiograph.

Artificial intelligence (AI) could be used as an automatically diagnosis method for dental disease due to its accuracy and efficiency. This research proposed a novel convolutional neural network (CNN)-based deep learning (DL) ensemble model for tooth position detection, tooth outline segmentation, tooth tissue segmentation, periodontal bone loss and periodontitis stage prediction using dental panoramic radiographs. The dental panoramic radiographs of 320 patients during the period January 2020 to December 2023 were collected in our dataset. All images were de-identified without private information. In total, 8462 teeth were included. The algorithms that DL ensemble model adopted include YOLOv8, Mask R-CNN, and TransUNet. The prediction results of DL method were compared with diagnosis of periodontists. The periodontal bone loss degree deviation between the DL method and ground truth drawn by the three periodontists was 5.28%. The overall PCC value of the DL method and the periodontists' diagnoses was 0.832 (P <​ 0.001). ​The ICC value was 0.806 (P <​ 0.001). The total diagnostic accuracy of the DL method was 89.45%. The proposed DL ensemble model in this study shows high accuracy and efficiency in radiographic detection and a valuable adjunct to periodontal diagnosis. The method has strong potential to enhance clinical professional performance and build more efficient dental health services. The DL method not only could help dentists for rapid and accurate auxiliary diagnosis and prevent medical negligence, but also could be used as a useful learning resource for inexperienced dentists and dental students.

Anchor carbon dots inside NH2-MIL-88B via ship-in-a-bottle strategy for dual signal enhancement in colorimetric-fluorescent sensors

The increase in oxytetracycline (OTC) pollution has become a significant risk to ecological stability and human health because of excessive use. Therefore, developing a precise and reliable sensor for trace OTC detection is critical. In this work, a dual-mode sensor of colorimetric-fluorescent (CL-FL) with dual signal-on was developed for OTC detection. Firstly, a novel carbon dots encapsulating in cavities of MOFs composite (CDs@NH2-MIL-88B) was constructed by the ship-in-a-bottle approach, which enhanced peroxidase-like activity and fluorescent intensity. Further, combining with surface molecularly imprinted polymer (MIP), the dual-mode sensor was constructed to develop selectivity (CDs@NH2-MIL-88B@rMIP). Finally, the dual signal enhancement mechanism of the sensor comes from the interaction between OTC and identifying binding site involving strong hydrogen bonding and metal complexation from imprinted cavity and CDs@NH2-MIL-88B, respectively. The prepared sensor has a linear detection range of 1.00 × 10-9 - 1.00 × 10-5M with the limit of detection (LOD) of 1.47 × 10-10M for the CL mode and a detection range of 1.00 × 10-11 -1.00 × 10-7M with the LOD of 1.84 × 10-12M for the FL mode. Meanwhile, the sensor shows high sensitivity, accuracy and reliability in OTC detection. Overall, this work provides a promising CL-FL dual-mode sensing systems with signal-on in the detection of antibiotics and other hazardous pollutants.

Design and experiment of a deviation information detection mechanism for sugar beet harvesters based on agricultural machinery and agronomy integration

To address large geometric shape differences, poor ridge distribution straightness and the uncertain spatial distribution of beet roots during the harvesting period, as well as damage caused by the inaccurate row correction detection mechanisms of combine harvesters, this study conducts a design analysis and performance tests of deviation information detection mechanisms based on the integration of agricultural machinery and agronomy. The agronomic process of mechanized sugar beet production was analyzed, the geometric dimensions of root tubers under typical planting patterns of typical varieties in main sugar beet production areas were measured, and a three-dimensional geometric model of beet root tubers was established. A device for measuring ridge shape and root tuber distribution was designed, and agronomic parameters during the harvesting period, such as plant spacing, ridge height, unearthed height, and deviation distance of beet root tubers, were measured and analyzed. A spatial model of ridge shape and beet root tuber growth distribution on the ridge during harvesting was established. The overall structure of the deviation information detection mechanism was analyzed and designed, and the structural forms and key parameters of key mechanisms, such as left‒right swing detection and up‒down floating profiling, were designed on the basis of agronomic parameter analysis. Using the missed detection rate as the evaluation index, field performance tests of the deviation information detection mechanism were conducted. The results revealed that when the average forward speed of the harvester was 0.84 m/s, the average missed detection rate of the deviation information detection mechanism was 0.56%. This method has high adaptability and detection accuracy and achieves a high level of integration for agricultural machinery (detection spatial region of the detection mechanism) and agronomy (beet root distribution spatial region). This study provides a technical basis and methodological reference for improving the quality and efficiency of automatic row correction combined with harvesting operations for crops such as sugar beets.

Advanced detection of foreign objects in fresh-cut vegetables using YOLOv5

The presence of foreign objects in fresh-cut vegetables has become a significant concern for the industry in recent years. Ensuring the safety of consumers and suppliers necessitates comprehensive precautionary measures. This study introduces a novel approach for detecting foreign objects in fresh-cut vegetables using YOLOv5. The results indicate that YOLOv5s excels in identifying foreign objects with a high detection accuracy of 98.30%, a rapid inference time of 2.6 ms, and a compact model size of 13.3 MB. The model effectively identified foreign objects such as transparent and colored plastic, paper, wood, stone, insects, glass, and metal. Moreover, YOLOv5s accurately detected foreign objects with color similarities to green onions, which is particularly challenging due to their wide color variation. Hence, the foreign objects dataset was tested and generated 98.63% and 98.67% for cabbage and green onion, respectively. Additionally, YOLOv5s successfully detected small foreign objects (2–3 mm) in two types of fresh-cut vegetables. Despite its excellent performance, the YOLOv5s model struggles to identify foreign objects that overlap with vegetable samples. To address this issue, installing an automatic conveyor unit could facilitate continuous sample movement, while a feeder unit could reduce the possibility of overlap. This research demonstrates the feasibility of implementing the YOLOv5s, as a non-destructive technique for detecting foreign objects in fresh-cut vegetables. The findings contribute to the development of an accurate, fast, and efficient real-time inspection system, with potential applications in the fresh-cut vegetable industry to enhance product quality and safety.

A Blockchain based Secure Authentication Technique for Ensuring User Privacy in Edge Based Smart City Networks

In the past decade, modernization of Information and Communication Technology (ICT), Edge Computing (EC), and Smart Cities has attracted significant academic interest due to its diverse applications in the fields of healthcare, transportation, agriculture, and defense. EC offers numerous advantages, including faster and more efficient services, lower latency, improved data processing, managed bandwidth consumption, scalable, real-time decision-making, security, reduced network congestion, and increased resilience. Despite these benefits, EC networks face persistent challenges, particularly related to security and privacy concerns. Addressing these security challenges requires strong authentication mechanisms, which demand extra resources like processing power and memory, often surpassing the limited capabilities of lightweight edge devices compared to cloud systems. This highlights the critical need for securing edge nodes and ensuring user privacy before real-world deployment and data transfer. User and edge device authentication is vital to prevent external and internal Impersonation and Reflection attacks that threaten system integrity and confidentiality. This paper presents a BlockChain based Authentication technique for Edge Networks (BCAuthEN) that utilizes a Consortium Blockchain (CB) with key agreements for biometric authentication, incorporating a Fuzzy Extractor (FE) to secure user biometrics and passwords. In addition, BCAuthEN offers multifactor and continuous authentication by monitoring user behavior and biometrics. BCAuthEN has been formally verified through Real-Or-Random (RoR) modeling and AVISPA tool, proving its effectiveness in enhancing privacy, and security. The proposed technique ensures robust security by preventing attackers at the potential entry points (edge nodes). In addition, BCAuthEN reduces computation cost, communication overhead and improves throughput. BCAuthEN provides strong resilience by achieving high detection accuracy and reduces false positives against impersonation and reflection attacks. Results have shown that BCAuthEN improves communication costs and reduces overhead by 10% and 7%, respectively, as compared to the recent biometric and key-based user authentication techniques.

XSShield: A Novel Dataset and Lightweight Hybrid Deep Learning Model for XSS Attack Detection

With the proliferation of web applications, cross-site scripting (XSS) attacks have increased significantly and now pose a significant threat to users' information security and privacy. To enhance the efficiency of XSS attack detection, the adoption of machine learning (ML) and deep learning (DL) techniques offers promising solutions, but their effectiveness is limited by the lack of comprehensive and diverse datasets. Moreover, existing approaches often prioritize detection accuracy over real-time processing capabilities, which are essential for effective defense. To address these challenges, in this paper, we propose a novel framework that automatically collects web resources, efficiently extracts informative features, and constructs an up-to-date XSS attack dataset, which is then used to train a machine learning-based XSS detection model. Using this framework, we created and published a well-structured dataset over 100,000 samples for the research community. Furthermore, we present a hybrid detection model that leverages the strengths of both Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. Extensive evaluations of our dataset demonstrate that the proposed model outperforms other baseline ML models across various metrics, including processing rate. Notably, our model achieves an accuracy of 99.27% while maintaining a low false positive rate of 0.06% and high processing rate of exceeding 1000 samples per second. These results highlight its high accuracy and robustness in detecting XSS, and suitability for real-time applications. Our work presents a comprehensive solution for enhancing web application security by providing a diverse dataset and a high-accuracy detection model with low latency.

Defect intelligent recognition of membrane product based on deep learning

Defect detection plays a crucial role in the manufacturing industry, ensuring the quality of industrial products. Despite advancements in this field, current defect detection methods face two primary challenges: (1) extracting visually similar features from the background poses difficult and (2) these methods struggle to identify tiny defects in the target objects. We present a feature enhancement module called the SE-CAR, which aims to handle the identified problems effectively. This module is designed to efficiently capture tiny defects in images, prioritize defect information features, and ultimately enhance the model’s predictive performance and accuracy in defect recognition tasks. In addition, Distance-IoU Non-Maximum Suppression is employed as a substitution for the original Non-Maximum Suppression. This enhances the recognition accuracy of bounding boxes, ensuring that the model maintains high detection accuracy even after complex scenarios. Moreover, the proposed methodology exhibits broad applicability across a wide spectrum of prevalent defect detection paradigms. In empirical experiments, we employ the YOLOv7 architecture as the foundation framework, integrating the proposed methodologies for the purpose of detecting defects in the membrane. The empirical evidence demonstrates a notable improvement in the performance of detecting defects in membrane products using the SE-CAR feature enhancement module and Distance-IoU Non-Maximum Suppression algorithm. In comparison to baseline networks, an increase in mAp by 6.29%, precision by 1.63%, and recall by 8.76%.

Efficient Vehicle Detection and Optimization in Multi-Graph Mode Considering Multi-Section Tracking Based on Geographic Similarity

Vehicle detection is an important part of modern intelligent transportation systems. At present, complex deep learning algorithms are often used for vehicle detection and tracking, but high-precision detection results are often obtained at the cost of time, and the existing research rarely considers optimization algorithms for vehicle information. Based on this, we propose an efficient method for vehicle detection in multi-graph mode and optimization method considering multi-section tracking based on geographic similarity. In this framework, we design a vehicle extraction method based on multi-graph mode and a vehicle detection technology based on traffic flow characteristics, which can cope with the challenge of vehicle detection under an unstable environment. Further, a multi-section tracking optimization technology based on geographic similarity at a high video frame rate is proposed, which can efficiently identify lane change behavior and match, track, and optimize vehicles. Experiments are carried out on several road sections, and the model performance and optimization effect are analyzed. The experimental results show that the vehicle detection and optimization algorithm proposed in this paper has the best effect and high detection accuracy and robustness. The average results of Recall, Precision, and F1 are 0.9715, 0.979, and 0.9752, respectively, all of which are above 0.97, showing certain competitiveness in the field of vehicle detection.

TSRDet: A Table Structure Recognition Method Based on Row-Column Detection

As one of the most commonly used and important data carriers, tables have the advantages of high structuring, strong readability and strong flexibility. However, in reality, tables usually present various forms, such as Excel, images, etc. Among them, the information in the table image cannot be read directly, let alone further applied. Therefore, the research related to image-based table recognition is crucial. It contains the table structure recognition and the table content recognition. Among them, table structure recognition is the most important and difficult task because the table structure is abstract and changeable. In order to address this problem, we propose an innovative table structure recognition method, named TSRDet (Table Structure Recognition based on object Detection). It includes a row-column detection method, named SACNet (StripAttention-CenterNet) and the corresponding post-processing. SACNet is an improved version of the original CenterNet. The specific improvements include the following: firstly, we introduce the Swin Transformer as the encoder to obtain the global feature map of the image. Then, we propose a plug-and-play row-column attention module, including a channel attention module and a row-column spatial attention module. It improves the detection accuracy of rows and columns by capturing long-range row-column feature maps in the image. After completing the row-column detection, this paper also designs a simple and fast post-processing to generate the table structure based on the row-column detection results. Experimental results show that for row-column detection, SACNet has high detection accuracy, even at a high IoU threshold. Specifically, when the threshold is 0.75, its mAP of row detection and column detection still exceeds 90%, which is 91.40% and 92.73% respectively. In addition, in the comparative experiment with the existing object detection methods, SACNet’s performance was significantly better than that of all others. For table structure recognition, the TEDS-Struct score of TSRDet is 95.7%, which shows competitive performance in table structure recognition, and verifies the rationality and superiority of the proposed method.

Research on Microscale Vehicle Logo Detection Based on Real-Time DEtection TRansformer (RT-DETR)

Vehicle logo detection (VLD) is a critical component of intelligent transportation systems (ITS), particularly for vehicle identification and management in dynamic traffic environments. However, traditional object detection methods are often constrained by image resolution, with vehicle logos in existing datasets typically measuring 32 × 32 pixels. In real-world scenarios, the actual pixel size of vehicle logos is significantly smaller, making it challenging to achieve precise recognition in complex environments. To address this issue, we propose a microscale vehicle logo dataset (VLD-Micro) that improves the detection of distant vehicle logos. Building upon the RT-DETR algorithm, we propose a lightweight vehicle logo detection algorithm for long-range vehicle logos. Our approach enhances both the backbone and the neck network. The backbone employs ResNet-34, combined with Squeeze-and-Excitation Networks (SENetV2) and Context Guided (CG) Blocks, to improve shallow feature extraction and global information capture. The neck network employs a Slim-Neck architecture, incorporating an ADown module to replace traditional downsampling convolutions. Experimental results on the VLD-Micro dataset show that, compared to the original model, our approach reduces the number of parameters by approximately 37.6%, increases the average accuracy (mAP@50:95) by 1.5%, and decreases FLOPS by 36.7%. Our lightweight network significantly improves real-time detection performance while maintaining high accuracy in vehicle logo detection.

Asymptomatic bacteriuria screening for developing countries using a modified water quality test kit.

Between 2% and 15% of pregnant women unknowingly experience asymptomatic bacteriuria (ASB), defined as ≥105 CFU per milliliter of urine in the absence of symptoms. ASB increases the risk of adverse pregnancy outcomes including pyelonephritis, preterm labor, and low-birth weight infants. While pregnant women in the United States are routinely screened for ASB, those in developing countries with limited resources and funding lack an accurate mechanism for ASB screening. Aquagenx water quality test kits detect Escherichia coli, the most common causative agent of ASB, and total coliform bacteria in drinking water via colorimetric and fluorescent indicators. We found that the Aquagenx system is compatible with human urine and then proceeded to develop an ASB screening protocol using disposable inoculating loops. Our protocol diagnosed artificial ASB- samples (104 CFU/mL E. coli) with a false positive (FP) rate of 33% (n = 18) and ASB+ (105 CFU/mL E. coli) with a false negative (FN) rate of 5.6% (n = 18). Clinical sample testing with our protocol revealed a FP rate of 0% in ASB- samples (n = 28) and a FN rate of 0% in ASB+ samples caused by coliforms (n = 13). Aquagenx did not detect ASB in nine clinical samples with non-coliform etiological agents due to the limitations of the technology. However, with very high accuracy for detection of E. coli and other causative agents that collectively account for 90.1% of ASB cases, these kits could be used as a diagnostic ASB screening tool in developing countries in which there is currently no alternative to urine culture.IMPORTANCEAsymptomatic bacteriuria (ASB) affects 2%-15% of pregnant women and can result in adverse maternal and fetal outcomes if left undetected and untreated. In the United States and other developed nations, pregnant women are regularly screened for ASB via urine culture. However, in low-resource countries where bacterial culture is not available, dipstick testing is used. Although accurate in cases of symptomatic bacteriuria, dipstick detection is ineffective for detecting ASB. Here, we made use of an existing water quality field test for ASB urine screening, which would be readily deployable in low-resource settings. We optimized a dilution protocol for sampling patient urine within the detection limits of the Aquagenx kit technology. Overall, we were able to detect ASB samples with Gram-negative pathogens that collectively account for 90% of all ASB cases. Utilization of this repurposed technology for proactive medical screening may help prevent adverse pregnancy and birth outcomes due to ASB.